Methods and apparatus to characterize households with media meter data

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data. An example method includes identifying, with a processor, a power status and a first automatic gain control (AGC) value for an exposure minute from a panelist audience meter in a first household, the panelist audience meter comprising a power sensor, identifying a second AGC value and a daypart for a household tuning minute from a first media meter (MM) in the first household, the MM comprising microphones to collect audio data, and calculating model coefficients based on the exposure minute and the household tuning minute to be applied to data from a second MM in a second household, the model coefficients to facilitate a power status probability calculation in the second household devoid of the panelist audience meter having the power sensor.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application Ser.No. 61/839,344, which was filed on Jun. 25, 2013, U.S. ProvisionalApplication Ser. No. 61/844,301, which was filed on Jul. 9, 2013, U.S.Provisional Application Ser. No. 61/986,409, which was filed on Apr. 30,2014, and U.S. Provisional Application Ser. No. 62/007,535, which wasfiled on Jun. 4, 2014, all of which are hereby incorporated herein byreference in their entireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to market research, and, moreparticularly, to methods and apparatus to characterize households withmedia meter data.

BACKGROUND

In recent years, panelist research efforts included installing meteringhardware in qualified households that fit one or more demographics ofinterest. In some cases, the metering hardware is capable of determiningwhether a media presentation device (such as a television set) ispowered on and tuned to a particular station via a hardwired connectionfrom the media presentation device to the meter. In other cases, themetering hardware is capable of determining which household member isexposed to a particular portion of media via one or more button presseson a People Meter by the household member near the television.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example media distribution environment in whichhouseholds may be characterized with media meter data.

FIG. 2 is a schematic illustration of an example imputation engineconstructed in accordance with the teachings of this disclosure.

FIG. 3 is a plot illustrating an example viewing index effect based onan age of collected data.

FIG. 4 is an example weighting allocation table to apply a temporalweight to collected minutes.

FIG. 5 is an example dimension subset map to illustrate independentdistribution of household dimensions used to characterize householdswith media meter data.

FIGS. 6-9 are flowcharts representative of example machine readableinstructions that may be executed to implement the example imputationengine of FIGS. 1 and 2.

FIG. 10 is an example visitor table to illustrate example visitor tuningminutes and exposure minutes for a demographic of interest.

FIG. 11 is a schematic illustration of an example visitor imputationengine constructed in accordance with the teachings of this disclosure.

FIG. 12 are example cell parameter calculations including exampledemographics of interest and example categories of interest to determineaverage visitor parameters to be used to impute a number of visitors.

FIG. 13 are example independent parameter calculations to determineaverage visitor parameters to be used to impute a number of visitors.

FIG. 14 are example probability values and cumulative probability valuesgenerated by the example visitors imputation engine of FIGS. 1 and 11.

FIG. 15 is a flowchart representative of example machine readableinstructions that may be executed to implement the example visitorimputation engine of FIGS. 1 and 11.

FIG. 16 is a schematic illustration of an example ambient tuning engineconstructed in accordance with the teachings of this disclosure

FIGS. 17-19 are flowcharts representative of example machine readableinstructions that may be executed to implement the example ambienttuning engine of FIGS. 1, 10 and 16.

FIG. 20 is an example crediting chart illustrating example categories ofcollected viewing minutes.

FIG. 21 is a schematic illustration of an example on/off detectionengine constructed in accordance with the teachings of this disclosure.

FIG. 22 is a flowchart representative of example machine readableinstructions that may be executed to implement the example on/offdetection engine of FIGS. 1 and 21.

FIG. 23 is a schematic illustration of an example processor platformthat may execute the instructions of FIGS. 6-9, 15, 17-19 and/or 22 toimplement the example ambient tuning engine, the example imputationengine and the example on/off detection engine of FIGS. 1, 2, 10, 16and/or 21.

DETAILED DESCRIPTION

Market researchers seek to understand the audience composition and sizeof media, such as radio programming, television programming and/orInternet media so that advertising prices can be established that arecommensurate with audience exposure and demographic makeup (referred toherein collectively as “audience configuration”). As used herein,“media” refers to any sort of content and/or advertisement which ispresented or capable of being presented by an information presentationdevice, such as a television, radio, computer, smart phone or tablet. Todetermine aspects of audience configuration (e.g., which householdmember is currently watching a particular portion of media and thecorresponding demographics of that household member), the marketresearchers may perform audience measurement by enlisting any number ofconsumers as panelists. Panelists are audience members (householdmembers) enlisted to be monitored, who divulge and/or otherwise sharetheir media exposure habits and demographic data to facilitate a marketresearch study. An audience measurement entity typically monitors mediaexposure habits (e.g., viewing, listening, etc.) of the enlistedaudience members via audience measurement system(s), such as a meteringdevice and a People Meter. Audience measurement typically involvesdetermining the identity of the media being displayed on a mediapresentation device, such as a television.

Some audience measurement systems physically connect to the mediapresentation device, such as the television, to identify which channelis currently tuned by capturing a channel number, audio signaturesand/or codes identifying (directly or indirectly) the programming beingdisplayed. Physical connections between the media presentation deviceand the audience measurement system may be employed via an audio cablecoupling the output of the media presentation device to an audio inputof the audience measurement system. Additionally, audience measurementsystems prompt and/or accept audience member input to reveal whichhousehold member is currently exposed to the media presented by themedia presentation device.

As described above, audience measurement entities may employ theaudience measurement systems to include a device, such as the PeopleMeter (PM), having a set of inputs (e.g., input buttons) that are eachassigned to a corresponding member of a household. The PM is anelectronic device that is typically disposed in a media exposure (e.g.,viewing) area of a monitored household and is proximate to one or moreof the audience members. The PM captures information about the householdaudience by prompting the audience members to indicate that they arepresent in the media exposure area (e.g., a living room in which atelevision set is present) by, for example, pressing their assignedinput key on the PM. When a member of the household selects theircorresponding input, the PM identifies which household member ispresent, which includes other demographic information associated withthe household member, such as a name, a gender, an age, an incomecategory, etc. However, in the event a visitor is present in thehousehold, the PM includes at least one input (e.g., an input button)for the visitor to select. When the visitor input button is selected,the PM prompts the visitor to enter an age and a gender (e.g., viakeyboard, via an interface on the PM, etc.).

The PM may be accompanied by a base metering device (e.g., a base meter)to measure one or more signals associated with the media presentationdevice. For example, the base meter may monitor a television set todetermine an operational status (e.g., whether the television is poweredon or powered off, a media device power sensor), and/or to identifymedia displayed and/or otherwise emitted by the media device (e.g.,identify a program being presented by a television set). The PM and thebase meter may be separate devices and/or may be integrated into asingle unit. The base meter may capture audience measurement data via acable as described above and/or wirelessly by monitoring audio and/orvideo output by the monitored media presentation device. Audiencemeasurement data captured by the base meter may include tuninginformation, signatures, codes (e.g., embedded into or otherwisebroadcast with broadcast media), and/or a number of and/oridentification of corresponding household members exposed to the mediaoutput by the media presentation device (e.g., the television).

Data collected by the PM and/or the base meter may be stored in a memoryand transmitted via one or more networks, such as the Internet, to adata store managed by a market research entity such as The NielsenCompany (US), LLC. Typically, such data is aggregated with datacollected from a large number of PMs and/or base meters monitoring alarge number of panelist households. Such collected and/or aggregateddata may be further processed to determine statistics associated withhousehold behavior in one or more geographic regions of interest.Household behavior statistics may include, but are not limited to, anumber of minutes a household media device was tuned to a particularstation, a number of minutes a household media device was used (e.g.,viewed) by a household panelist member and/or one or more visitors,demographics of an audience (which may be statistically projected basedon the panelist data) and instances when the media device is on or off.While examples described herein employ the term “minutes,” such as“household tuning minutes,” “exposure minutes,” etc., any other timemeasurement of interest may be employed without limitation.

To ensure audience measurement systems are properly installed inpanelist households, field service personnel have traditionally visitedeach panelist household, assessed the household media components,physically installed (e.g., connected) the PM and/or base meter tomonitor a media presentation device(s) of the household (e.g., atelevision), and trained the household members how to interact with thePM so that accurate audience information is captured. In the event oneor more aspects of the PM and/or base meter installation areinadvertently disrupted (e.g., an audio cable connection from the mediadevice to the base meter is disconnected), then subsequent field servicepersonnel visit(s) may be necessary. In an effort to allow collectedhousehold data to be used in a reliable manner (e.g., a mannerconforming to accepted statistical sample sizes), a relatively largenumber of PMs and/or base meters are needed. Each such PM and/or basemeter involves one or more installation efforts and installation costs.As such, efforts to increase statistical validity (e.g., by increasingpanel size and/or diversity) for a population of interest result in acorresponding increase in money spent to implement panelist householdswith PMs and/or base meters.

In an effort to increase a sample size of household behavior data and/orreduce a cost associated with configuring panelist households with PMsand/or base meters, example methods, apparatus, systems and/or articlesof manufacture disclosed herein employ a media meter (MM) to collecthousehold panelist behavior data. Example MMs disclosed herein aredistinguished from traditional PMs and/or base meters that include aphysical connection to the media presentation device (e.g., atelevision). In examples disclosed herein, the MM captures audio withouta physical connection to the media device. In some examples, the MMincludes one or more microphones to capture ambient audio in a roomshared by the media device. In some such examples, the MM captures codesembedded by one or more entities (e.g., final distributor audio codes(FDAC)), and does not include one or more inputs that are to be selectedby one or more household panelists to identify which panelist iscurrently viewing the media device. Rather than collecting audiencecomposition data directly from panelists, example methods, apparatus,systems and/or articles of manufacture disclosed herein apply one ormore models to impute which household members are exposed to particularmedia programming to collected MM data. Such example imputationtechniques are described in further detail below and referred to hereinas “persons imputation.” Additionally, example methods, apparatus and/orarticles of manufacture disclosed herein apply one or more models toimpute a number of visitors in each household and correspondingage/demographic characteristics of such visitors. In other words,examples disclosed herein facilitate a manner of determining aprobability of household exposure activity, a number of visitors and/orcorresponding visitor ages in a stochastic manner that avoids theexpense of additional PM device installation in panelist households.

In some examples, a household includes two or more media devices, suchas a first television located in a first room and a second televisionlocated in a second room. In the event the panelist household includesfirst and second meters physically connected to the first and secondtelevisions, then the physical connection unambiguously identifies whichaudio data is originating from which television in the household, evenif such audio from the first television propagates to the second roomhaving the second television (and/or vice versa). Circumstances in whichmedia played in one room can be heard and/or otherwise detected inanother room (which may also have a media presentation device andaccompanying meter) are referred to herein as “spillover.” In the eventthe panelist household includes first and second MMs located in thefirst and second rooms, respectively, then spillover audio data “heard”(detected) from the first room may erroneously be credited by the secondMM as media presented in the second room (and/or vice versa). Mediatuning events logged by a MM as occurring in one room, but actuallyoccurring in a second different room (e.g., due to spillover) arereferred to herein as “ambient tuning.” In other words, because the MMincludes microphones to collect audio emitted from media devices, thepossibility exists that the first MM in the first room is picking-upand/or otherwise detecting audio from the media device in an adjacent(e.g., the second) room. Ambient tuning is distinguished from “realtuning” in that real tuning occurs when the MM properly credits themedia presentation device (e.g., television) associated with the room inwhich the MM is located with a media exposure for media actuallypresented on that media presentation device. Example methods, apparatus,systems and/or articles of manufacture disclosed herein apply models toidentify instances of ambient tuning (e.g., due to spillover) asdistinguished from real (legitimate) tuning. Similarly, example methods,apparatus, systems and/or articles of manufacture disclosed herein applymodels to identify instances of when a media presentation device isturned on as distinguished from instances of when the media device ispowered off. This is important in avoiding crediting of media exposurewhen no such exposure is occurring. For example, in the event ahousehold member is in a first room with an associated mediapresentation device in a powered-off state, but the associated meter inthat first room is detecting audio from a second media device in asecond room, examples disclosed herein identify the occurrence asspillover and do not credit the detection as an actual media exposure.

Turning to FIG. 1, an example media distribution environment 100includes a network 102 (e.g., the Internet) communicatively connected topanelist households within a region of interest (e.g., a target researchgeography 104). In the illustrated example of FIG. 1, some panelisthouseholds 106 include People Meters (PMs) and media meters (MMs) 106and some other panelist households 108 include only MMs to capturehousehold media exposure information. Households having both MMs and PMsare referred to herein as MMPM households 106. Households that do nothave a PM, but have a MM are referred to herein as MMHs (media meterhouseholds) 108. Behavior information collected by the example MMPMs 106and the example MMHs 108 are sent via the example network 102 to anexample imputation engine 110, an example visitor imputation engine, anexample ambient tuning engine 120, and/or an example on/off detectionengine 130 for analysis. As described above, because MMHs 108 do notinclude PMs, they do not include physical button inputs to be selectedby household members to identify which household member is currentlywatching particular media, and they do not include physical buttoninputs to be selected by household visitors to identify age and/orgender information. Therefore, example methods, systems, apparatusand/or articles of manufacture disclosed herein model householdcharacteristics that predict a likelihood that a particular householdmember is watching the identified media being accessed in the MMHs 108.

Example households that include a PM collect panelist audience data. Asused herein, “panelist audience data” includes both (a) mediaidentification data (e.g., code(s) embedded in or otherwise transmittedwith media, signatures, channel tuning data, etc.) and (b) personinformation identifying the corresponding household member(s) and/orvisitors that are currently watching/viewing/listening to and/orotherwise accessing the identified media. On the other hand, MMHhouseholds 108 include only a MM to collect media data. As used herein,“media data” and/or “media identifier information” are usedinterchangeably and refer to information associated with mediaidentification (e.g., codes, signatures, etc.), but does not includeperson information identifying which household member(s) and/or visitorsare currently watching/viewing/listening to and/or otherwise accessingthe identified media. As described in further detail below, examplemethods, apparatus, systems and/or articles of manufacture disclosedherein impute person identifying data to media data collected from MMHhousehold(s) 108.

Although examples disclosed herein refer to code readers and collectingcodes, techniques disclosed herein could also be applied to systems thatcollect signatures and/or channel tuning data to identify media. Audiowatermarking is a technique used to identify media such as televisionbroadcasts, radio broadcasts, advertisements (television and/or radio),downloaded media, streaming media, prepackaged media, etc. Existingaudio watermarking techniques identify media by embedding one or moreaudio codes (e.g., one or more watermarks), such as media identifyinginformation and/or an identifier that may be mapped to media identifyinginformation, into an audio and/or video component. In some examples, theaudio or video component is selected to have a signal characteristicsufficient to hide the watermark. As used herein, the terms “code” or“watermark” are used interchangeably and are defined to mean anyidentification information (e.g., an identifier) that may be transmittedwith, inserted in, or embedded in the audio or video of media (e.g., aprogram or advertisement) for the purpose of identifying the media orfor another purpose such as tuning (e.g., a packet identifying header).As used herein “media” refers to audio and/or visual (still or moving)content and/or advertisements. To identify watermarked media, thewatermark(s) are extracted and used to access a table of referencewatermarks that are mapped to media identifying information.

Unlike media monitoring techniques based on codes and/or watermarksincluded with and/or embedded in the monitored media, fingerprint orsignature-based media monitoring techniques generally use one or moreinherent characteristics of the monitored media during a monitoring timeinterval to generate a substantially unique proxy for the media. Such aproxy is referred to as a signature or fingerprint, and can take anyform (e.g., a series of digital values, a waveform, etc.) representativeof any aspect(s) of the media signal(s) (e.g., the audio and/or videosignals forming the media presentation being monitored). A goodsignature is one that is repeatable when processing the same mediapresentation, but that is unique relative to other (e.g., different)presentations of other (e.g., different) media. Accordingly, the term“fingerprint” and “signature” are used interchangeably herein and aredefined herein to mean a proxy for identifying media that is generatedfrom one or more inherent characteristics of the media.

Signature-based media monitoring generally involves determining (e.g.,generating and/or collecting) signature(s) representative of a mediasignal (e.g., an audio signal and/or a video signal) output by amonitored media device and comparing the monitored signature(s) to oneor more references signatures corresponding to known (e.g., reference)media sources. Various comparison criteria, such as a cross-correlationvalue, a Hamming distance, etc., can be evaluated to determine whether amonitored signature matches a particular reference signature. When amatch between the monitored signature and one of the referencesignatures is found, the monitored media can be identified ascorresponding to the particular reference media represented by thereference signature that with matched the monitored signature. Becauseattributes, such as an identifier of the media, a presentation time, abroadcast channel, etc., are collected for the reference signature,these attributes may then be associated with the monitored media whosemonitored signature matched the reference signature. Example systems foridentifying media based on codes and/or signatures are long known andwere first disclosed in Thomas, U.S. Pat. No. 5,481,294, which is herebyincorporated by reference in its entirety.

Persons Imputation

FIG. 2 is a schematic illustration of an example implementation of theimputation engine 110 of FIG. 1. In the illustrated example of FIG. 2,the imputation engine 110 includes the visitor imputation engine 112, aPeople Meter (PM) interface 202, a media meter (MM) interface 204, acategorizer 206, a weighting engine 210 and a probability engine 212. Asdescribed in further detail below, the example visitor imputation engine112 employs one or more portions of the example imputation engine 110.The example probability engine 212 of FIG. 2 includes an exampledimension manager 214, an example cell probability engine 216 and anexample independent distribution engine 218. The example cellprobability engine 216 of FIG. 2 includes an example category fitmanager 220, an example minutes aggregator 222 and an example imputationengine 224. The example independent distribution engine 218 of FIG. 2includes an example category qualifier 226, an example proportionmanager 228 and an example distribution engine 230.

In operation, the example PM interface 202 acquires people meter datafrom any and all PMs within the example panelist households 104. Inparticular, the example PM interface 202 acquires PM data from the PMdevices located in the example MMPM households 106 (i.e., householdsthat have both MM devices and PM devices). The PM devices have input(s)(e.g., buttons for each household member to select to identify theirrespective presence in the audience currently exposed to media). In someexamples, the MMPM households 106 are associated with a particulargeographic area of focus, such as nationwide (sometimes referred to as a“National People Meter” (NPM)), while in other examples the MMPMhouseholds 106 are associated with a subset of a particular geographicarea of focus, such as a localized geography of interest (e.g., a citywithin a nation (e.g., Chicago), and sometimes referred to as “LocalPeople Meter” (LPM)).

For example, in the event an analysis of the Charlotte designated marketarea (DMA) is desired, then the example PM interface 202 captures datafrom LPM households within a time zone corresponding to the desired DMA(e.g., the Eastern time zone). In some examples, desired data may bestreamed back to one or more storage repositories, from which theexample imputation engine 110, the example ambient tuning engine 120and/or the example on/off detection engine 130 may retrieve the data.The example PM interface 202 of the illustrated examples collects,acquires and/or otherwise captures PM data (panelist audience data) frompanelist households 104 (having both PMs and MMs) and records oraggregates the media exposure minutes to respective persons within thehousehold as one or more of the possible audience members (e.g.,viewers) of the corresponding media. In other words, the capturedpanelist audience data is at a persons-level rather than at a householdlevel, which facilitates an ability to generate person probabilities, asdescribed in further detail below.

The example categorizer 206 of FIG. 2 categorizes the acquired panelistaudience data in any number of categories, such as by age, by gender, bywhether a household is of size one (e.g., a single person household) orof size two or more (e.g., two or more persons in the household), by astation/affiliate, by a genre and/or by daypart. In some examples,categories include those related to race, ethnicity, geography,language, metro vs. non-metro, etc. In still other examples, categoriesinclude an age of the head of household, a room location (e.g., a livingroom, a master bedroom, other bedroom, etc.), and/or the presence ofchildren. In the event one or more categories improve results, it may beused for analysis, while categories that do not illustrate improvementsor cause negative impacts may be removed during the analysis.

As used herein, categories refer to classifications associated withcollected exposure minutes (also known as “viewing minutes”). Categoriesmay include, but are not limited to, a daypart associated with collectedexposure minutes (e.g., Monday through Friday from 5:00 AM to 6:00 AM,Sunday from 10:00 PM to 1:00 AM, etc.), a station associated withcollected exposure minutes (e.g., WISN, WBBM, etc.), an age/genderassociated with collected exposure minutes (e.g., males age 2-5, femalesage 35-44, etc.), and a genre (e.g., kids programs, home repairprograms, music programs, sports programs, etc.) associated withcollected exposure minutes. In still other examples, the categorizer 206categorizes the acquired panelist audience data by education (e.g., 8years or less, 9 years to high school graduate, some college to Bachelordegree, master's degree or higher, etc.), life stage (e.g., pre-family,young family, older family, post family, retired, etc.) and/or a numberof media presentation devices (e.g., television sets in the household.One or more combinations of station/affiliate/genre/demographicattribute(s) may be categorized in different ways based on, for example,variations between data available for one or more age/gender levels. Forexample, some local markets have ten stations in which a sample size formen age 45-54 may exhibit a data sample size of statistical significancefor seven of those ten stations. In other examples, a local market mayhave relatively fewer stations where the age/gender levels are ofsufficient size to support statistical significance. In some suchexamples, the age/gender groupings are adjusted (e.g., from males age40-45 to males age 40-50) to increase an available sample size toachieve a desired statistical significance.

To impute panelist audience data (e.g., exposure minutes, which issometimes referred to herein as “viewing minutes”) to media data, theexample PM interface 202 identifies Local People Meter (LPM) data thathas been collected within a threshold period of time. On a relativescale, when dealing with, for example, television exposure, an exposureindex, which provides an indication of how well LPM data accuratelyimputes exposure minutes, may be computed in a manner consistent withEquation (1).

$\begin{matrix}{{{Exposure}\mspace{14mu}{Index}} = \frac{{{No}.\mspace{14mu}{of}}\mspace{14mu}{imputed}\mspace{14mu} L\; P\; M\mspace{14mu}{exposure}\mspace{14mu}{\min.\mspace{14mu}{for}}\mspace{14mu}{{ea}.\mspace{14mu}{cat}.}}{{{No}.\mspace{14mu}{of}}\mspace{14mu}{actual}\mspace{14mu} L\; P\; M\mspace{14mu}{exposure}\mspace{14mu}{\min.\mspace{14mu}{for}}\mspace{14mu}{{ea}.\mspace{14mu}{cat}.}}} & {{Equation}\mspace{14mu}(1)}\end{matrix}$In the illustrated example of Equation (1), the exposure index iscalculated as the ratio of the number of imputed LPM viewing minutes foreach category of interest and the number of actual LPM viewing minutesfor each category of interest.

The example exposure index of Equation (1) may be calculated on amanual, automatic, periodic, aperiodic and/or scheduled basis toempirically validate the success and/or accuracy of imputation effortsdisclosed herein. Index values closer to one (1) are indicative of agreater degree of accuracy when compared to index values that deviatefrom one (1). Depending on the type of category associated with thecollected exposure minutes, corresponding exposure index values may beaffected to a greater or lesser degree based on the age of the collecteddata. FIG. 3 is an example plot 300 of exposure index values by daypart.In the illustrated example of FIG. 3, the plot 300 includes an x-axis ofdaypart values 302 and a y-axis of corresponding exposure index values304. Index value data points labeled “1-week” appear to generally residecloser to index values of 1.00, while index value data points labeled“3-weeks” appear to generally reside further away from index values of1.00. In other words, panelist audience data that has been collectedmore recently results in index values closer to 1.00 and, thus, reflectsan imputation accuracy better than panelist audience data that has beencollected from longer than 1-week ago.

As described above, collected data that is more recent exhibits animputation accuracy that is better than an imputation accuracy that canbe achieved with relatively older collected data. Nonetheless, some datathat is relatively older will still be useful, but such older data isweighted less than data that is more recent to reflect its loweraccuracy. The example weighting engine 210 applies a temporal weight,and applies corresponding weight values by a number of days since thedate of collection. Relatively greater weight values are applied to datathat is relatively more recently collected. In some examples, weightvalues applied to collected tuning minutes and collected exposureminutes are based on a proportion of a timestamp associated therewith.For instance, a proportionally lower weight may be applied to a portionof collected minutes (e.g., tuning minutes, exposure minutes) when anassociated timestamp is relatively older than a more recently collectionportion of minutes.

FIG. 4 illustrates an example weighting allocation table 400 generatedand/or otherwise configured by the example weighting engine 210. In theillustrated example of FIG. 4, a MMPM household 106 acquired exposureminutes (i.e., individualized panelist audience data) via a PM device(row “A”), and acquired household tuning minutes (i.e., minutes tuned ina household without individualizing to a specific person within thathousehold) via a MM device (row “B”). The example individualizedpanelist audience and household tuning minutes are collected over aseven (7) day period. In that way, the most recent day (current day 402)is associated with a weight greater than any individualized panelistaudience and/or household tuning minutes from prior day(s). The exampleindividualized panelist minutes of row “A” may be further segmented inview of a desired category combination for a given household. Asdescribed above, categories that characterize a household may include aparticular age/gender, size of household, viewed station, daypart,number of televisions, life stage, education level and/or otherdemographic attribute(s). For purposes of illustration, examplesdescribed below, the household age/gender category for the household ismale, age 45-54, the tuned station is associated with a premium paychannel (genre) during the daypart associated with Monday through Fridaybetween 6:00 PM and 7:00 PM.

In the illustrated example of FIG. 4, the weighting engine 210 applies aunitary weight value to the first six (6) days of individualizedpanelist minutes and household tuning minutes, and applies a weightvalue of six (6) to the most current day. While a value of six (6) isdisclosed above, like the other values used herein, such value is usedfor example purposes and is not a limitation. In operation, the exampleweighting engine 210 of FIG. 2 may employ any weighting value in whichthe most current day value is relatively greater than values for one ormore days older than the current day. The example weighting engine 210may generate a weighted sum of the collected individualized panelistaudience exposure minutes (hereinafter referred to herein as “exposureminutes”) in a manner consistent with example Equation (2), and maygenerate a weighted sum of the collected household tuning minutes in amanner consistent with example Equation (3).

$\begin{matrix}{{{Exposure}\mspace{14mu}{{Min}.}} = {\left\lbrack {W_{1}\left( {\sum\limits_{d = 1}^{n}{EM}_{d}} \right)} \right\rbrack + \left\lbrack {W_{2}{EM}_{c}} \right\rbrack}} & {{Equation}\mspace{14mu}(2)} \\{{{Tuning}\mspace{14mu}{{Min}.}} = {\left\lbrack {W_{1}\left( {\sum\limits_{d = 1}^{n}{TM}_{d}} \right)} \right\rbrack + \left\lbrack {W_{2}{TM}_{c}} \right\rbrack}} & {{Equation}\mspace{14mu}(3)}\end{matrix}$In the illustrated examples of Equation (2) and Equation (3), W₁reflects a relatively lower weighting value than W₂, in which W₂ is theweighting value associated with the current day exposure minutes value.Additionally, d reflects one of n days of the collected data prior tothe current day, EM_(d) reflects exposure minutes for corresponding daysprior to the current day, TM_(d) reflects household tuning minutes forcorresponding days prior to the current day, EM_(c) reflects exposureminutes for the current day, and TM_(c) reflects household tuningminutes for the current day.

In connection with example data shown in the illustrated example of FIG.4 (e.g., days one through six having 20, 10, 10, 0, 0 and 10 exposureminutes, respectively, the current day having 40 exposure minutes, daysone through six having 40, 30, 50, 0, 0 and 30 household tuning minutesand the current day having 50 household tuning minutes), application ofexample Equation (2) results in a weighted exposure minutes value of 290and application of example Equation (3) results in a weighted householdtuning minutes value of 450. In some examples, the probability engine212 calculates an imputation probability that a MM panelist (e.g., apanelist household with only a MM device and no associated PM device)with the aforementioned category combination of interest (e.g., male,age 45-54 tuned to a premium pay channel during Monday through Fridaybetween the daypart of 6:00 PM and 7:00 PM) is actually viewing thistuning session. The imputation probability is calculated by the exampleprobability engine 212 by dividing the weighted exposure minutes (e.g.,290 minutes) by the weighted household tuning minutes (e.g., 450minutes) to yield a 64.4% chance that the MM panelist with this samehousehold category combination is associated with this tuning behavior.While examples disclosed herein refer to probability calculations, insome examples odds may be calculated to bound results between values ofzero and one. For example, odds may be calculated as a ratio of aprobability value divided by (1-Probability). If desired, the odds maybe converted back to a probability representation.

However, while the market researcher may have a particular categorycombination of interest, a corresponding probability value accuracy maybe improved when different probability calculation techniques areapplied in view of corresponding available sample sizes of householdssharing the particular category combination of interest. As described infurther detail below, if collected LPM data associated with the categorycombination of interest (e.g., male, age 45-54, tuned to premium channelduring 6:00 PM to 7:00 PM with three household members, one televisionand the head of household have some college credit or a bachelor'sdegree) is greater than a threshold value, then a cell probabilitytechnique may yield a probability value with acceptable accuracy. Asused herein, an acceptable accuracy relates to a sample size that iscapable and/or otherwise required to establish results having astatistical significance. However, in the event the collected LocalPeople Meter (LPM) data associated with the category combination ofinterest falls below the threshold value, then the cell probabilitytechnique yields unacceptably low probability value accuracy. Instead,example methods, apparatus, systems and/or articles of manufacturedisclosed herein employ independent distribution probabilitycalculations when the collected LPM data associated with the categorycombination of interest is below a threshold value, such as below athreshold value that is capable of facilitating one or more calculationsto yield results having statistical significance.

The example category manager 214 of FIG. 2 identifies categories and/ora category combinations of interest and determines whether theparticular category combination of interest has a threshold number ofhouseholds within a donor pool. As described above, the donor pool maybe a localized geography (a Local People Meter (LPM), such as thepanelist households within the geographic region of interest 104).However, as a geographic region of interest decreases in size, acorresponding number of qualifying households that match the categorycombination of interest also decreases. In some cases, the number ofqualifying households is below a threshold value, which causes one ormore traditional probability calculation methods (e.g., cellprobability) to exhibit poor predictive abilities and/or results thatfail to yield statistical significance. On the other hand, in the eventthe donor pool of households exceeds a threshold value count, then suchtraditional probability calculation methods (e.g., cell probability)exhibit satisfactory predictive capabilities under industry standard(s).

In operation, the example category manager 214 of FIG. 2 generates alogical “AND” condition test for a set of categories of interest. Forexample, if the categories of interest include (1) a particular station,(2) a particular daypart, (3) a particular number of household members,(4) a particular age, (5) a particular gender, (6) a particular numberof television sets in the household, (7) a particular education level ofthe head of household, and (8) a particular life stage, then thecategory manager 214 determines whether the combination of all eightcategories of interest are represented by a threshold number ofhouseholds within the donor pool. If so, then the example categorymanager 214 invokes the example cell probability engine 216 to calculatea probability value of the category combination occurring within MMHhouseholds 108. Generally speaking, when a number of households sharingthe combination of categories of interest (e.g., items (1) through (8)above) are greater than the threshold value, a corresponding level ofconfidence in probability calculation via the cell probability techniqueis deemed satisfactory.

In the event a market researcher seeks probability information for amale aged 50 watching a premium pay channel between the hours of 6:00 PMand 6:30 PM, the example category fit manager 220 of the illustratedexample identifies which previously established category groups alreadyexist that would best fit this desired task. In other words, thespecific and/or otherwise unique research desires of the marketresearcher may not align exactly with existing categorical groupscollected by LPM and/or NPM devices. Instead, the example category fitmanager 220 identifies that the closest categorical combination ofindustry standard and/or otherwise expected data is with males age 45-54between the hours of 6:00 PM and 7:00 PM. The example minutes aggregator222 of the illustrated example identifies a total number of householdtuning minutes in all households associated with the identified closestcategorical combination, and also identifies a total number of exposureminutes associated with the males age 45-54 in such households. Forexample, the minutes aggregator 222 may identify forty-five (45)qualifying households that have males 45-54 (e.g., the household couldhave more than just the males 45-54) in which a premium pay genrestation was tuned between the hours of 6:00 PM to 7:00 PM, threehousehold members with one television set and a head of household havingsome college credit or bachelor's degree.

Within these forty-five (45) qualifying households, the tuning minutesaggregator 222 may identify two-hundred (200) household tuning minutestotal, but only one hundred and two (102) of those minutes wereassociated with the males 45-54. The example imputation engine 224 ofthe illustrated example calculates a probability for imputation as theratio of exposure minutes for the males 45-54 and the total householdtuning minutes for all qualifying households in a manner consistent withexample Equation (4).

$\begin{matrix}{{{Probability}\mspace{14mu}{of}\mspace{14mu}{Imputation}} = \frac{{Exposure}\mspace{14mu}{Minutes}\mspace{14mu}{by}\mspace{14mu}{Persons}\mspace{14mu}{of}\mspace{14mu}{Interest}}{{Tuning}\mspace{14mu}{Minutes}\mspace{14mu}{of}\mspace{14mu}{Qualifying}\mspace{14mu}{Households}}} & {{Equation}\mspace{14mu}(4)}\end{matrix}$In the illustrated example of Equation (4), the probability ofimputation using the examples disclosed above is 0.51 (i.e., 102exposure minutes divided by 200 tuning minutes, in this example). Insome examples, the probability value calculated by the example cellprobability engine 216 is retained and/or otherwise imputed to MMHhouseholds 108 based on a normal distribution, such as a comparison ofthe calculated probability value to a random or pseudo-random number. Inthe event the calculated probability value is greater than the randomnumber, then the household member having the categorical combination ofinterest is credited as viewing a tuning segment. In other words, thehousehold tuning data is imputed to the MMH household 108 as exposuredata for the categorical combination of interest. On the other hand, inthe event the calculated probability value is less than the random orpseudo-random number, then the household member having the categoricalcombination of interest is not credited as viewing the tuning segment.In other words, the household tuning data is not imputed to the MMHhousehold 108.

As discussed above, when the combinations of all categories of interestare represented by a number of households less than a threshold valuewithin the donor pool, the cell probability calculation approach may notexhibit a level of confidence deemed suitable for statistical research.Generally speaking, a number of households in a research geography ofinterest matching a single one of the categories of interest may berelatively high. However, as additional categories of interest areadded, the number of households having an inclusive match for all suchcategories decreases. In some circumstances, the number of matchinghouseholds available in the donor pool after performing a logical “AND”of all categories of interest eventually results in a donor pool havinga population lower than a threshold value, which may not exhibitstatistical confidence when applying the cell probability techniquedescribed above. In such examples, the probability engine 212 prevents atraditional cell probability technique from being employed to calculatea probability of whether a household of interest should be credited withexposure behavior for the categorical combination of interest (e.g.,whether the male age 45-54 of the household should be credited withcaptured exposure (tuning) behavior of the household). Instead, theexample probability engine 212 invokes the example independentdistribution engine 218 when the number of households having the desiredcombination of categories of interest is below a threshold value. Asdescribed in further detail below, instead of using a pool of householdsthat match all categories of interest, households are employed thatmatch some of the categories of interest are used when calculating aprobability of viewing.

In operation, the example category qualifier 226 of FIG. 2 identifiesall households within the donor pool (e.g., within the LPM collectiongeography, such as the Charlotte DMA) that have the same set of keypredictors (i.e., particular categories within the set of categories ofinterest). In some examples, key predictors reflect a set of categoriesthat exhibit a relatively greater degree of success than othercombinations of categories. For instance, a first set of key predictorsmay include a first set of categories related to a geography ofinterest, such as sunscreen products in geographic vicinity to oceanvacation areas, or skiing products in geographic vicinity to mountainranges. While examples disclosed herein refer to a Local People Meter(LPM), such examples are not limited thereto. In some examples, aNational People Meter (NPM) may be employed as a collection geographythat reflects a relatively larger area, such as a nation. In particular,a subset of the example eight (8) original categories of interest mayinclude (1) households matching a household size category, (2)households matching a same member gender category, and (3) householdsmatching a same member age category. In other words, while the originaleight example categories of interest included the aforementioned threecategories, the remaining categories are removed from consideration whenidentifying households from the available data pool. For example, theremaining categories are removed that are related to (4) householdsmatching a same tuned station category, (5) households matching a sameeducation category, (6) households matching a same number of televisionsets category, (7) households matching a same daypart category, and (8)households matching a same life stage/household size category.

Because, in the illustrated example, the donor pool is constructed withonly MMPM households 106, the example category qualifier 226 retrievesand/or otherwise obtains a total household tuning minutes value and atotal exposure minutes value for the available households meeting thesize/gender/age criteria of interest (e.g., dimensions (1), (2) and (3)from above). For example, if the size/gender/age criteria of interest isfor a household size of two or more people having a male age 45-54, thenthe example category qualifier 226 identifies a number of householdsfrom that size/gender/age subset.

FIG. 5 illustrates an example category subset map 500 created by theindependent distribution engine 226 of the example of FIG. 2. Theexample independent distribution engine assembles household tuningminutes and exposure minutes from subsets of the categories of interest.In the illustrated example of FIG. 5, the map 500 includes a totalhousehold tuning minutes count and a total exposure minutes countassociated with the key predictor categories 502 of size/age/gender. Inthis example, the category qualifier 226 identified a total oftwo-hundred (200) households matching the size/gender/age criteria. Thetwo-hundred households include a total of 4500 tuning minutes (i.e.,minutes that identify a tuned station but do not identify acorresponding household member) and a total of 3600 exposure minutes(e.g., minutes for an identified station and also identified individualswho were present in the audience).

The example proportion manager 228 of FIG. 2 selects one or moreremaining categories of interest that fall outside the key predictorcategories to determine corresponding available matching households,household tuning minutes and exposure minutes. The example remainingcategories may be referred to as secondary predictors or secondarycategories that affect the probability of media exposure. While examplekey predictor categories disclosed herein include household size, genderand age, example methods, apparatus, systems and/or articles ofmanufacture may include any other, additional and/or alternate type(s)of categories for the key predictors. Additionally, while examplesecondary categories disclosed herein include tuned station, education,number of media presentation devices (e.g., TV sets), daypart andlifestage, example methods, apparatus, systems and/or articles ofmanufacture may additionally and/or alternatively include any other typeof categories as the secondary categories.

For example, the proportion manager 228 of the illustrated exampleselects one or more secondary categories to determine a correspondingnumber of matching households, household tuning minutes and exposureminutes. Again, and as described above, the temporal units of “minutes”are employed herein as a convenience when discussing example methods,apparatus, systems and/or articles of manufacture disclosed herein, suchthat one or more additional and/or alternative temporal units (e.g.,seconds, days, hours, weeks, etc.) may be considered, withoutlimitation. In the illustrated example of FIG. 5, a tuned stationcategory 504 (e.g., one of the secondary categories of interest) isidentified by the proportion manager 228 to have eighty (80) households,which match the desired station of interest (e.g., station “WAAA”), inwhich those households collected 1800 household tuning minutes and 1320exposure minutes. Additionally, the example proportion manager 228 ofFIG. 2 selects an education category 506 (e.g., one of the secondarycategories of interest) and determines that one-hundred and ten (110)households match the desired education level of interest (e.g.,households in which the head of household has 9 years of school to highschool graduation), in which those households collected 1755 householdtuning minutes and 1200 exposure minutes. Further, the exampleproportion manager 228 of FIG. 2 selects a number of television setscategory 508 (e.g., one of the secondary categories of interest) anddetermines that one-hundred (100) households match the desired number ofTV sets within a household value, in which those households collected2100 household tuning minutes and 1950 exposure minutes. Other examplecategories considered by the example proportion manager 228 of FIG. 2include a daypart category 510 (e.g., one of the secondary categories ofinterest), in which the proportion manager 228 of FIG. 2 determines thatone-hundred (100) households match the desired daypart category, inwhich those households collected 1365 household tuning minutes and 825exposure minutes. The example proportion manager 228 of FIG. 2 alsoselects a life stage/household size category 512 (e.g., one of thesecondary categories of interest) and determines that seventy (70)households match the desired type of life stage/household size value, inwhich those households collected 1530 household tuning minutes and 1140exposure minutes.

Generally speaking, the proportion manager 228 of the illustratedexample identifies secondary category contributions of household tuningminutes and exposure minutes independently from the household tuning andexposure minutes that may occur for only such households that match allof the desired target combination of categories of interest. After eachindividual secondary category contribution household tuning minute valueand exposure minute value is identified, the example distribution engine230 calculates a corresponding household tuning proportion and exposureproportion that is based on the key predictor household tuning andexposure minute values. As described in further detail below, theexample distribution engine 230 calculates a household tuning proportionand an exposure proportion associated with each of the secondarycategories of interest (e.g., the tuned station category 504, theeducation category 506, the number of sets category 508, the daypartcategory 510 and the life stage/size category 512). In other words,examples disclosed herein capture, calculate and/or otherwise identifycontributory effects of one or more secondary categories of interest bycalculating and/or otherwise identifying a separate corresponding tuningproportion and separate corresponding exposure proportion for each oneof the secondary categories. As described in further detail below,separate contributory effects of the one or more secondary categoriesare aggregated to calculate expected tuning minutes and expectedexposure minutes.

In the illustrated example of FIG. 5, the distribution engine 230divides the household tuning minutes associated with the tuned stationcategory 504 (e.g., 1800 household tuning minutes) by the totalhousehold tuning minutes associated with the key predictor categories502 (e.g., 4500 household tuning minutes) to calculate a correspondingtuned station category tuning proportion 514. Additionally, thedistribution engine 230 of the illustrated example divides the exposureminutes associated with the tuned station category 504 (e.g., 1320exposure minutes) by the total exposure minutes associated with the keypredictor categories 502 (e.g., 3600 household viewing minutes) tocalculate a corresponding tuned station category viewing proportion 516.For the sake of example, the calculated tuned station category tuningproportion 514 is 0.40 (e.g., 1800 household tuning minutes divided by4500 total exposure minutes) and the calculated tuned station categoryviewing proportion 516 is 0.37 (e.g., 1320 exposure minutes divided by3600 total exposure minutes).

The example distribution engine 230 of FIG. 2 also calculates ahousehold tuning proportion and exposure proportion in connection withthe example education category 506. In the illustrated example of FIG.5, the distribution engine 230 divides the household tuning minutesassociated with the education category 504 (e.g., 1755 household tuningminutes) by the total household tuning minutes associated with the keypredictor categories 502 (e.g., 4500 household tuning minutes) tocalculate a corresponding education category household tuning proportion518. Additionally, the example distribution engine 230 of theillustrated example divides the exposure minutes associated with theeducation category 506 (e.g., 1200 exposure minutes) by the totalexposure minutes associated with the key predictor categories 502 (e.g.,3600 exposure minutes) to calculate a corresponding education categoryexposure proportion 520. For the sake of example, the calculatededucation category household tuning proportion 518 is 0.39 (e.g., 1755household tuning minutes divided by 4500 total household tuning minutes)and the calculated education category exposure proportion 520 is 0.33(e.g., 1200 exposure minutes divided by 3600 total exposure minutes).

The example distribution engine 230 of FIG. 2 also calculates ahousehold tuning proportion and exposure proportion in connection withthe example household sets category 508. In the illustrated example ofFIG. 5, the distribution engine 230 divides the household tuning minutesassociated with the household sets category 508 (e.g. 2100 householdtuning minutes) by the total household tuning minutes associated withthe key predictor categories 502 (e.g., 4500 household tuning minutes)to calculate a corresponding household sets category household tuningproportion 522. Additionally, the example distribution engine 230 of theillustrated example divides the exposure minutes associated with thehousehold sets category 508 (e.g., 1950 exposure minutes) by the totalexposure minutes associated with the key predictor categories 502 (e.g.,3600 exposure minutes) to calculate a corresponding household setscategory exposure proportion 524. For the sake of example, thecalculated household sets category household tuning proportion 522 is0.47 (e.g., 2100 household tuning minutes divided by 4500 totalhousehold tuning minutes) and the calculated household sets categoryexposure proportion 524 is 0.54 (e.g., 1950 exposure minutes divided by3600 total exposure minutes).

The example distribution engine 230 of FIG. 2 also calculates ahousehold tuning proportion and exposure proportion in connection withthe example daypart category 510. In the illustrated example of FIG. 5,the distribution engine 230 divides the household tuning minutesassociated with the daypart category 510 (e.g., 1365 household tuningminutes) by the total household tuning minutes associated with the keypredictor categories 502 (e.g., 4500 household tuning minutes) tocalculate a corresponding daypart category household tuning proportion526. Additionally, the example distribution engine 230 of FIG. 2 dividesthe exposure minutes associated with the daypart category 510 (e.g., 825exposure minutes) by the total exposure minutes associated with the keypredictor categories 502 (e.g., 3600 exposure minutes) to calculate acorresponding daypart category exposure proportion 528. For the sake ofexample, the calculated daypart category household tuning proportion 526is 0.30 (e.g., 1365 household tuning minutes divided by 4500 totalhousehold tuning minutes) and the calculated daypart category exposureproportion 528 is 0.23 (e.g., 825 exposure minutes divided by 3600 totalexposure minutes).

The example distribution engine 230 of FIG. 2 also calculates ahousehold tuning proportion and exposure proportion in connection withthe example life stage/size category 512. In the illustrated example ofFIG. 5, the distribution engine 230 divides the household tuning minutesassociated with the life stage/size category 512 (e.g. 1530 householdtuning minutes) by the total household tuning minutes associated withthe key predictor categories 502 (e.g., 4500 household tuning minutes)to calculate a corresponding life stage/size category household tuningproportion 530. Additionally, the example distribution engine 230 ofFIG. 2 divides the exposure minutes associated with the life stage/sizecategory 512 (e.g., 1140 exposure minutes) by the total exposure minutesassociated with the key predictor categories 502 (e.g., 3600 exposureminutes) to calculate a corresponding life stage/size category exposureproportion 532. In this example, the calculated life stage/size categorytuning proportion 530 is 0.34 (e.g., 1530 household tuning minutesdivided by 4500 total household tuning minutes) and the calculated lifestage/size category exposure proportion 532 is 0.32 (e.g., 1140 exposureminutes divided by 3600 total exposure minutes).

As described above, each of the target combinations of categories ofinterest has an independently calculated household tuning proportionvalue and an independently calculated exposure proportion value. Theexample distribution engine 230 of FIG. 2 calculates the product of allhousehold tuning proportion values (e.g., the tuned station categoryhousehold tuning proportion 514, the education category household tuningproportion 518, the household sets category household tuning proportion522, the daypart category household tuning proportion 526, and the lifestage/size category household tuning proportion 530) to determine totalexpected household tuning minutes 534. Additionally, the exampledistribution engine 230 of FIG. 2 calculates the product of allhousehold exposure proportion values (e.g., the tuned station categoryexposure proportion 516, the education category exposure proportion 520,the household sets category exposure proportion 524, the daypartcategory exposure proportion 528, and the life stage/size categoryexposure proportion 532) to determine total expected exposure minutes536. A final independent distribution is calculated by the exampledistribution engine 230 in a manner consistent with example Equation(5), and reflects a panelist behavior probability associated with thetarget combination of categories of interest.

$\begin{matrix}{{{Independent}\mspace{14mu}{Distribution}\mspace{14mu}{Probability}} = \frac{{Expected}\mspace{14mu}{Exposure}\mspace{14mu}{Minutes}}{{Expected}\mspace{14mu}{Household}\mspace{14mu}{Tuning}\mspace{14mu}{Minutes}}} & {{Equation}\mspace{14mu}(5)}\end{matrix}$

In the example exposure and household tuning minutes discussed above,the resulting independent distribution probability is 0.52. In effect,the resulting independent distribution probability is interpreted as amale 45-54 who lives in a three (3) person household, classified as anolder family, with a head of house education of nine (9) years to highschool graduate, with two (2) television sets in the household, has a52% likelihood of watching station WAAA during the daypart of Mondaythrough Friday from 9:00 AM to 12:00 PM.

While an example manner of implementing the imputation engine 110 ofFIG. 1 is illustrated in FIGS. 2-5, one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Additionally, while an example manner of implementing the visitorimputation engine 112 of FIGS. 1, 2 and 11, and described in furtherdetail below, one or more of the elements, processes and/or devicesillustrated in FIG. 11 may be combined, divided, rearranged, omitted,eliminated and/or implemented in any other way. Additionally, while anexample manner of implementing the ambient tuning engine 120 and theexample on/off detection engine 130 of FIG. 1 is illustrated in FIGS. 10and 15, respectively, and as described in further detail below, one ormore of the elements, processes and/or devices illustrated in FIGS. 10and 15 may be combined, divided, rearranged, omitted, eliminated and/orimplemented in any other way. Further, the example people meterinterface 202, the example categorizer 206, the example weighting engine210, the example media meter interface 204, the example probabilityengine 212, the example category manager 214, the example cellprobability engine 216, the example category fit manager 220, theexample minutes aggregator 222, the example imputation engine 224, theexample independent distribution engine 218, the example categoryqualifier 226, the example proportion manager 228, the exampledistribution engine 230 and/or, more generally, the example imputationengine 110 and/or the example visitor imputation engine 112 of FIG. 1may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Additionally, anexample average visitor parameter (AVP) calculator 1102, an exampledistribution engine 1104, an example random number generator 1106, anexample visitor assignor 1108, an example simultaneous tuning monitor1602, an example crediting manager 1604, an example station comparator1606, an example tuning type assignor 1608, an example automatic gaincontrol monitor 1610, an example code presence manager 1612, an examplemodeling engine 1614, an example code stacking manager 1616 and/or, moregenerally, the example ambient tuning engine 120 of FIGS. 1 and 16 maybe implemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample people meter interface 202, the example categorizer 206, theexample weighting engine 210, the example media meter interface 204, theexample probability engine 212, the example category manager 214, theexample cell probability engine 216, the example category fit manager220, the example minutes aggregator 222, the example imputation engine224, the example independent distribution engine 218, the examplecategory qualifier 226, the example proportion manager 228, the exampledistribution engine 230, the example average visitor parameter (AVP)calculator 1102, an example distribution engine 1104, an example randomnumber generator 1106, an example visitor assignor 1108, the examplesimultaneous tuning monitor 1602, the example crediting manager 1604,the example station comparator 1606, the example tuning type assignor1608, the example automatic gain control monitor 1610, the example codepresence manager 1612, the example modeling engine 1614, the examplecode stacking manager 1616 and/or, more generally, the exampleimputation engine 110, the example visitor imputation engine 112, theexample ambient tuning engine 120, and/or the example on/off detectionengine 130 of FIG. 1 could be implemented by one or more analog ordigital circuit(s), logic circuits, programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)).

When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example people meter interface 202, the example categorizer 206, theexample weighting engine 210, the example media meter interface 204, theexample probability engine 212, the example category manager 214, theexample cell probability engine 216, the example category fit manager220, the example minutes aggregator 222, the example imputation engine224, the example independent distribution engine 218, the examplecategory qualifier 226, the example proportion manager 228, the exampledistribution engine 230, the example average visitor parameter (AVP)calculator 1102, an example distribution engine 1104, an example randomnumber generator 1106, an example visitor assignor 1108, the examplesimultaneous tuning monitor 1602, the example crediting manager 1604,the example station comparator 1606, the example tuning type assignor1608, the example automatic gain control monitor 1610, the example codepresence manager 1612, the example modeling engine 1614, the examplecode stacking manager 1616 and/or, more generally, the exampleimputation engine 110, the example visitor imputation engine 112, theexample ambient tuning engine 120, and/or the example on/off detectionengine 130 of FIG. 1 is/are hereby expressly defined to include atangible computer readable storage device or storage disk such as amemory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. storing the software and/or firmware. Further still, theexample imputation engine 110, the example visitor imputation engine112, the example ambient tuning engine 120, and/or the example on/offdetection engine 130 of FIGS. 1, 2, 11, 16 and/or 21 may include one ormore elements, processes and/or devices in addition to, or instead of,those illustrated in FIGS. 2, 11, 16 and/or 21 and/or may include morethan one of any or all of the illustrated elements, processes anddevices.

Flowcharts representative of example machine readable instructions forimplementing the imputation engine 110, the visitor imputation engine112, the ambient tuning engine 120 and the on/off detection engine 130of FIGS. 1, 2, 11, 16 and 21 are shown in FIGS. 6-9, 15, 17-19 and 22.In these examples, the machine readable instructions comprise program(s)for execution by a processor such as the processor 2312 shown in theexample processor platform 2300 discussed below in connection with FIG.23. The program(s) may be embodied in software stored on a tangiblecomputer readable storage medium such as a CD-ROM, a floppy disk, a harddrive, a digital versatile disk (DVD), a Blu-ray disk, or a memoryassociated with the processor 2312, but the entire program(s) and/orparts thereof could alternatively be executed by a device other than theprocessor 2312 and/or embodied in firmware or dedicated hardware.Further, although the example program(s) is/are described with referenceto the flowcharts illustrated in FIGS. 6-9, 15, 17-19 and 22, many othermethods of implementing the example imputation engine 110, the examplevisitor imputation engine 112, the example ambient tuning engine 120and/or the example on/off detection engine 130 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined.

As mentioned above, the example processes of FIGS. 6-9, 15, 17-19 and 22may be implemented using coded instructions (e.g., computer and/ormachine readable instructions) stored on a tangible computer readablestorage medium such as a hard disk drive, a flash memory, a read-onlymemory (ROM), a compact disk (CD), a digital versatile disk (DVD), acache, a random-access memory (RAM) and/or any other storage device orstorage disk in which information is stored for any duration (e.g., forextended time periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 6-9, 15, 17-19 and 22may be implemented using coded instructions (e.g., computer and/ormachine readable instructions) stored on a non-transitory computerand/or machine readable medium such as a hard disk drive, a flashmemory, a read-only memory, a compact disk, a digital versatile disk, acache, a random-access memory and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm non-transitory computer readable medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, when the phrase “at least” is used as the transition termin a preamble of a claim, it is open-ended in the same manner as theterm “comprising” is open ended.

The program 600 of FIG. 6 begins at block 602 where the example peoplemeter interface 202 acquires PM data associated with household membersfrom the PM devices located in the example MMPM households 106 that haveboth MM devices and PM devices. As described above, the PM devices haveinput(s) (e.g., buttons for each household member and a visitor buttonto identify their respective presence in the audience currently exposedto media). The example PM interface 202 identifies collected data thatis within a threshold period of time from a current day in an effort toweight such data according to its relative age. As described above inconnection with example Equation (1), an accuracy of the viewing indexis better when the corresponding collected data is more recent. Theexample categorizer 206 categorizes the acquired PM data based on one ormore categories of interest (block 604). In some examples, thecategorizer 206 categorizes and/or otherwise identifies particularhouseholds associated with one or more categories, such as an age/gendercombination of interest, a particular household size of interest, aparticular life stage of interest, a particular viewedstation/affiliate/genre of interest, a particular daypart of interest, anumber of television sets of interest within the household (e.g.,households with one television set, households with 2-3 television sets,households with three or more television sets, etc.), and/or aneducation level of the head of household. While a relatively largenumber of MMPM households 106 will have at least one of theaforementioned categories, a substantially smaller number of MMPMhouseholds 106 will represent all of the target combination ofcategories of interest to a market researching during a market study.

As described above in connection with FIG. 4, the example weightingengine 210 applies weights in proportions that are based on a number ofdays since the date of collection of the donor data (block 606). Theexample media meter interface 204 also acquires household tuning datafrom media meters in the MMH households 108 (block 608). Depending onwhether a threshold number of households exist in the donor pool (e.g.,the donor pool of MMPM households in the region of interest 104) thatmatch all of the categories of interest, the example probability engine212 will invoke a corresponding probability calculation technique (block610) as described in further detail below in connection with FIG. 7.

FIG. 7 includes additional detail from the illustrated example of FIG.6. When generating probabilities, the example category manageridentifies categories of interest to use. Generally speaking, examplemethods, apparatus, systems and/or articles of manufacture disclosedherein generate probabilities based on a target combination ofcategories of interest such as, for example, determining the likelihoodof viewing for (1) a male age 45-54 (2) who lives in a three-personhousehold, (3) classified as an older family (4) with the head of thehousehold having an education of nine (9) years of school to high-schoolgraduate, (5) with two television sets in the household and (6) iswatching station WAAA (7) between the daypart of 9:00 AM to 12:00 PM.The example category manager 214 identifies categories of interest forwhich a probability of viewing (exposure) is desired (block 702), suchas the example seven categories referred-to above. Based on theidentified target combination of categories of interest, such as theexample above having the male age 45-54 et al., the example categorymanager 214 determines whether the available pool of data, previouslyweighted by the example weighting engine 210, includes a thresholdnumber of households that match all (e.g., all seven) of the targetcombination of categories of interest (block 704).

Assuming, for the sake of example, the threshold number of households tomatch all of the categories of interest is thirty (30), and the pool ofdata includes that threshold amount of available households (block 704),the example cell probability engine 216 is invoked by the probabilityengine 212 to calculate a probability value via a cell probabilitytechnique (block 706). On the other hand, if the pool of data does notsatisfy the threshold amount of thirty households (e.g., has less than30 households) (block 704), then the example probability engine 212invokes the example independent distribution engine 218 to calculate aprobability value via an independent distribution technique (block 708).

FIG. 8 illustrates an example manner of implementing the cellprobability calculation (block 706) of FIG. 7. In the illustratedexample of FIG. 8, the category fit manager 220 culls and/or otherwiselimits tuning and viewing data to fit previously established categories(block 802). As described above, in the event a market researcher has aninterest for a male age 50, industry standard panelist data acquisitiontechniques may not exactly fit the desired demographic category.Instead, the industry standard available data may be categorized interms of males between an age range of 45-54. Because the desiredcategory of interest is for a male age 50, the example category fitmanager 220 identifies the closest relevant category grouping that willsatisfy the market researcher, which in this example, includes the groupof men between the ages of 45-54. The example minutes aggregator 222identifies a total number of household tuning minutes from the selectedcategory (block 804) and identifies a total number of exposure minutesfrom the selected category (block 806). In other words, of all thehouseholds that match the categories of men age 45-54, the total numberof household tuning minutes and exposure minutes are identified.

The example imputation engine 224 of FIG. 2 calculates a probability forimputation based on the aforementioned totals (block 808). As describedabove, the probability of imputation may be calculated by the exampleimputation engine 224 in a manner consistent with example Equation (4).The example imputation engine 224 invokes a random number generator togenerate a random or pseudo-random number (block 810) and, if theresulting random or pseudo-random number is less than or equal to theprobability value (block 812), a household member within a householdhaving a media meter 108 is assigned as a viewer of the tuning segment(block 814). On the other hand, in the event the resulting random orpseudo-random number is not less than or equal to the probability value,then the household member within the household having the media meter108 is not assigned as a viewer of the tuning segment (block 816).

Returning to block 704 of FIG. 7, and continuing with the assumptionthat the threshold number of households to match all of the categoriesof interest is thirty (30), and the pool of data fails to include thatthreshold number of qualifying households (block 704), then the exampleindependent distribution engine 218 is invoked by the probability engine212 to calculate a probability value via an independent distributiontechnique (block 710).

FIG. 9 illustrates an example implementation of the independentdistribution probability calculation (block 708) of FIG. 7. In theillustrated example of FIG. 9, the category qualifier 226 identifies allpanelist households (e.g., LPM, NPM, etc.) within the donor pool thathave the same set of key predictors (block 902). Additionally, theexample category qualifier 226 identifies a corresponding number oftotal tuning minutes associated with the key predictors, and acorresponding number of total household exposure minutes associated withthe key predictors. As described above, key predictors may refer to aparticular combination of a household size, a gender of interest withinthe household, and/or an age of interest within the household. Forexample, the category qualifier 226 may identify all households withinthe donor pool that have two or more household members, in which one ofthem is a male age 45-54. For illustration purposes, assume the examplecategory qualifier identified two-hundred (200) households that have twoor more members therein, in which one of them is a male age 45-54. Alsoassume that the combined number of identified households (200) reflect4500 total household tuning minutes and 3600 total exposure minutes.

In addition to key predictors having an influence on the probability ofviewing, one or more additional secondary predictors may also influencethe probability of viewing. As described above, the market researchermay have a combined set or target combination of categories of interest,but a number of households having all of those combined set ofcategories of interest does not exceed a threshold value (e.g., thirty(30) households). However, while the combined set of categories ofinterest may not be represented en masse from the donor pool, subportions of the combined set or target combination of categories mayinclude a relatively large representation within the donor pool. Examplemethods, apparatus, systems and/or articles of manufacture disclosedherein identify independent sub portions (subgroups) of the combined setof categories of interest and corresponding households associated witheach subgroup of interest, which are applied independently to calculatea household exposure probability.

The example proportion manager 228 identifies a number of householdsfrom the key predictors group (e.g., 200 households having a size 2+ anda male age 45-54) that match a subgroup of interest (block 904). Fromthe subgroup of interest, the example proportion manager 228 identifiesa number of household tuning minutes and divides that value by the totalhousehold tuning minutes to calculate a household tuning proportionassociated with the subgroup of interest (block 906). For example, ifthe subgroup of interest is all households tuned to the same station(e.g., WAAA) (e.g., the tuned station category) and such householdsreflect 1800 tuning minutes, then the example proportion manager 228divides 1800 by the total household tuning minutes of 4500 to calculatea tuned station category household tuning proportion of 0.40 (block906). The example proportion manager 228 also identifies a number ofexposure minutes and divides that value by the total exposure minutes tocalculate an exposure proportion associated with the subgroup ofinterest (e.g., the example tuned station category) (block 908). Forexample, if the subgroup of interest is all households tuned to the samestation (e.g., WAAA) (e.g., the household tuned station dimension) andsuch households reflect 1320 exposure minutes, then the exampleproportion manager 228 divides 1320 by the total exposure minutes of3600 to calculate a tuned station category exposure proportion of 0.37(block 908). If more subgroups of interest from the donor pool areavailable (block 910), then the example proportion manager 228 selectsthe next subgroup of interest (block 912) and control returns to block904.

After category household tuning proportion values and exposureproportion values have been calculated for each subgroup of interest,the example distribution engine 230 calculates the product of allhousehold tuning proportion values and the total household tuningminutes (e.g., 4500 in this example) from the categories of interest(block 914), and calculates the product of all exposure proportionvalues and the total exposure minutes (e.g., 3600 in this example) fromthe categories of interest (block 916). A final independent distributionprobability may then be calculated as the ratio of the exposure minutesand the household tuning minutes in a manner consistent with exampleEquation (5). For example, and as described above in connection withFIG. 5, the resulting ratio of expected exposure minutes (17.47) andexpected household tuning minutes (33.65) may be a value of 0.52. Thisresulting ratio indicates a 52% likelihood that the panelist member is amale age 45-54 that lives in a three person household, classified as anolder family, with the head of household education of 9 years to highschool graduate, with two television sets in the household, and watchingstation WAAA on Mondays through Fridays between 9:00 AM to 12:00 PM.

Visitor Imputation

As disclosed above, persons imputation utilizes who is in the householdand what the household viewed such that for a given tuning segment, oneor more household members may be assigned and/or otherwise associatedwith exposure. However, panelist households may have visitors that areexposed to media within the household, in which the available visitorinformation is limited to an age and a gender. As described above, theexample PM includes inputs (e.g., buttons) for each household member aswell as button(s) for entering age and gender information for anyvisitors interacting with the media device (e.g., a television). Examplemethods, apparatus, systems and/or articles of manufacture disclosedherein apply a model to, in view of collected panelist household visitorinformation, determine a number and corresponding age/gender of visitorsfor households that do not employ a PM.

Visitor imputation disclosed herein exhibits some similarities topersons imputation, and aspects of FIGS. 1-9 will be referred to in thefollowing disclosure, as necessary. For example, both the personsimputation disclosed above and the visitor imputation disclosed belowutilize tuning and exposure information to assign tuning segments andcalculate ratios of exposure to tuning minutes. However, the visitorimputation viewing/tuning ratio, being the ratio of total visitorexposure to total household-level tuning exposure, reflects an averagecount of visitor exposure and not a probability. FIG. 10 furtherillustrates a manner in which visitor information is processed ascompared to household member exposure information.

In the illustrated example of FIG. 10, information for a first household1002, a second household 1004 and a third household 1006 exhibit twelve,fifteen and eight minutes, respectively, of time tuned by a particularstation of interest (as determined by each household with both MMdevices and PM devices). While the illustrated example of FIG. 10 onlyincludes three households, such example is for illustrative purposesonly and any number of household may be considered. One member of thefirst household 1002 was exposed to seven minutes out of twelve totaltuning minutes, which results in a probability of viewing of 7/12(58.3%). In the second household 1004, a first member was exposed to thefull fifteen minutes, while a second member was exposed to five minutesof the tuned duration, resulting in a probability of viewing of(15+5)/(15+15) (66.7%). In the third household 1006, a first member ofthat household was exposed to the full eight minutes, resulting in aprobability of viewing of 8/8 (100%). An overall viewing probability forthe example households is determined in a manner consistent with exampleEquation 6.

$\begin{matrix}{{{Probability}\mspace{14mu}{Viewed}} = {\frac{\sum{{HH}\mspace{14mu}{Member}\mspace{14mu}{Exposure}\mspace{14mu}{Minutes}}}{\sum{{HH}\mspace{14mu}{Tuning}\mspace{14mu}{Minutes}\mspace{14mu}\left( {{by}\mspace{14mu}{Person}} \right)}}.}} & {{Equation}\mspace{14mu} 6}\end{matrix}$In the illustrated example of Equation 6, HH refers to household, andapplying the example data from FIG. 10 to Equation 6 is shown in exampleEquation 7.

$\begin{matrix}{= {\frac{(7) + \left( {15 + 5} \right) + (8)}{(12) + \left( {15 + 15} \right) + (8)} = {\frac{35}{50} = {{.70}.}}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$In the illustrated example of Equation 7, the households of interest forthe example demographic group of males age 25-34 have a viewingprobability of 0.70. However, the following analysis of visitors in thesame households of interest calculates an average visitor viewing ratioin a manner consistent with example Equation 8.

$\begin{matrix}{{{{Ave}.\mspace{14mu}{Visitor}}\mspace{14mu}{Viewing}} = {\frac{\sum\;{{Visitor}\mspace{14mu}{Exposure}\mspace{14mu}{Minutes}\mspace{14mu}{for}\mspace{14mu}{Each}\mspace{14mu}{Person}}}{\sum\;{{Household}\mspace{14mu}{Tuning}\mspace{14mu}{Minutes}}}.}} & {{Equation}\mspace{14mu} 8}\end{matrix}$Applying the example data from FIG. 10 to Equation 8 is shown in exampleEquation 9.

$\begin{matrix}{= {\frac{\left( {12 + 10} \right) + (15) + (5)}{(12) + (15) + (8)} = {\frac{42}{35} = {1.20.}}}} & {{Equation}\mspace{14mu} 9}\end{matrix}$In the illustrated example of Equation 9, the households of interest forthe visitors that are reporting male age 25-34 exhibit an average of1.20 minutes of viewing time for each tuned minute.

FIG. 11 is a schematic illustration of an example implementation of theexample visitor imputation engine 112 of FIG. 1. The example visitorimputation engine 112 of FIG. 1 is constructed in accordance with theteachings of this disclosure, and includes an average visitor parameter(AVP) calculator 1102, a distribution engine 1104, a random numbergenerator 1106, and a visitor assignor 1108. As described above,operation of the example visitor imputation engine 112 may occur inconjunction with one or more portions of the example imputation engine110 of FIGS. 1 and 2. In operation, the example people meter interface202 acquires PM data associated with visitors, in which the PM data isfrom the PM devices located in the example MMPM households 106 that haveboth MM devices and PM devices. The example PM interface 202 identifiescollected visitor data that is within a threshold period of time from acurrent day in an effort to weight such data according to its relativeage, as described above in connection with example Equation (1).

The example visitor imputation engine 112 invokes the examplecategorizer 206 and/or example category qualifier 226 to categorize theacquired PM visitor data based on one or more categories of interest. Asdescribed above, for a given category or categories of interest,particular households associated with such categories are identified.Depending on whether a threshold number of households exist in the donorpool of visitor data that match all of the desired categories ofinterest, the example AVP calculator 1102 will invoke a correspondingAVP calculation technique. For example, if more than a threshold numberof households exist that have the desired categories of interest (e.g.,30 households), then the cell category approach may be used to calculateAVP, while the independent category approach may be used to calculateAVP, such as the independent category approach described in connectionwith example FIG. 5.

In the event the threshold number of households exist for a given set ofcategories of interest, the example AVP calculator 1102 calculates theAVP in a manner consistent with example Equation 8, and shown in FIG.12. In the illustrated example of FIG. 12, categories of interestinclude particular tuning characteristics 1202 (e.g., households thatwatch Disney station between 12:30 and 5:00 PM on Mondays throughFridays) and particular household characteristics 1204 (e.g., householdsin an Older Family Life Stage with 2 television sets). Additionally, theexample analysis of FIG. 12 is performed for two types of visitors; oneassociated with females age 6-11 (column 1206) and one associated withmales age 55-64 (column 1208). Among households that matched the desiredset of characteristics of interest exhibited (as determined by collectedPM visitor data), there were 3,892 minutes of visitor female age 6-11exposure (cell 1210) and 3,109 total household tuning minutes (cell1212). Application of example Equation 8 yields an AVP of 1.252 (cell1214) for such visitors that are female age 6-11. Additionally,households that matched the desired set of characteristics of interestexhibited 1,081 minutes of visitor male age 55-64 exposure (cell 1216),and the total household tuning minutes (cell 1218) remains the same at3,109. Application of example Equation 8 yields an AVP of 0.348 (cell1220) for such visitors that are male age 55-64.

On the other hand, in the event a threshold number of households are notavailable for the desired categories of interest (e.g., less than 30households), then the example AVP calculator 1102 calculates the AVP ina manner consistent with Equation 8 after determining expected exposureminutes and expected tuning minutes as category proportions, asdescribed above in connection with FIG. 5. FIG. 13 illustrates exampletuning data and exposure data for target demographic of females age6-11, where the threshold number of households meeting the categorycombination of interest (e.g., life-stage=older family plus TV sets=2)were not available. In the illustrated example of FIG. 13, householdsreflecting the category “Life Stage=Older Family” exhibited 443,940female age 6-11 visitor exposure minutes (cell 1302) and 733,317 tuningminutes (cell 1304), and households reflecting the category “TV Sets=2”exhibited 150,844 female age 6-11 visitor exposure minutes (cell 1306)and 285,877 tuning minutes (cell 1308). Additionally, a total amount offemale age 6-11 visitor exposure minutes exhibited 1,741,474 minutes(cell 1310), and a total amount of household tuning minutes exhibited8,200,347 minutes (cell 1312).

The example AVP calculator 1102 and/or the example distribution engine230 calculates an exposure proportion for each category of interest 1314and a tuning proportion for each category of interest 1316. Continuingwith the illustrated example of FIG. 13, the exposure proportionassociated with the life stage category is the ratio of visitor exposureminutes to total viewing minutes to yield a proportion factor of 0.255(result 1318). Additionally, the exposure proportion associated with theTV sets category is 0.087 (result 1320). The example tuning proportionassociated with the life stage category is the ratio of household tuningminutes to total tuning minutes to yield a tuning proportion of 0.089(result 1322), and a tuning proportion of 0.035 associated with the TVsets category (result 1324). While the illustrated example of FIG. 13includes two (2) categories of interest, example methods, apparatus,systems and/or articles of manufacture may include any number ofcategories of interest.

The example AVP calculator 1102 calculates an expected exposure minutesvalue (cell 1326) as the product of the total exposure minutes (cell1310) and any number of calculated exposure proportion values that occurbased on the number of categories of interest (e.g., result 1318 andresult 1320). The example AVP calculator 1102 also calculates anexpected tuning minutes value (cell 1328) as the product of the totaltuning minutes (cell 1312) and any number of calculated tuningproportion values that occur based on the number of categories ofinterest (e.g., result 1322 and result 1324). In a manner consistentwith example Equation 8, the example AVP calculator 1102 calculates theAVP value (cell 1330), which is used to determine a number of visitorsand associated ages, as described in further detail below.

To determine a number of visitors and corresponding ages, examplemethods, apparatus, systems and/or articles of manufacture disclosedherein employ a distribution model. While the type of distribution modeldescribed below is a Poisson distribution, this distribution is used forexample purposes and not limitation. The Poisson distribution is adiscrete probability distribution to express the probabilities of givennumbers of events when their average rate is known, and applied hereinto assign a number of visitors watching a given tuning segment (thepreviously calculated AVP being the known average rate). Probabilitiesfor the Poisson distribution are defined in a manner consistent withexample Equation 10.

$\begin{matrix}{{p(v)} = {\frac{\left( {\lambda_{d}^{v}*{\mathbb{e}}^{- \lambda_{d}}} \right)}{v!}.}} & {{Equation}\mspace{14mu} 10}\end{matrix}$In the illustrated example of Equation 10, v reflects a number ofvisitors, p(v) reflects a probability calculated for “v” visitors, andλ_(d) reflects the AVP for a given demographic group of interest (e.g.,female age 6-11). The example distribution engine 1104 defines thedistribution, such as the example Poisson distribution above, andcalculates probability values for a candidate number of visitors ofinterest, as shown in further detail in FIG. 14.

In the illustrated example of FIG. 14, eleven (11) different number ofvisitor values 1402 are selected by the example distribution engine 1104for a first demographic group of interest 1404 (e.g., female age 6-11),and eleven (11) different number of visitor values 1406 are selected bythe example distribution engine 1104 for a second demographic group ofinterest 1408 (e.g., male age 55-64). For each discrete number ofvisitor value, the example distribution engine 1104 calculates acorresponding probability value (see row 1410 associated with femalesage 6-11, and see row 1412 associated with males age 55-64). The exampledistribution engine 1104 also calculates the corresponding cumulativeprobabilities c(v) within each demographic group of interest (see row1414 associated with females age 6-11, and see row 1416 associated withmales age 55-64). The example cumulative distribution of FIG. 14 allowsarrangement of the probabilities between boundaries of zero and one as amatter of convenience such that the example random number generator 1106can identify a lookup value.

For each demographic group of interest, the example visitor assignor1108 invokes the random number generator 1106 to generate a randomnumber that, when referenced against the cumulative distribution values,reveals a number of visitors to attribute to that demographic group ofinterest. For example, if the random number generator produces a valueof 0.757000 for the first group 1404 associated with females age 6-11,then this value is associated by the example visitor assignor 1108 tofall within a visitor (v) value of 2. Additionally, if the random numbergenerator produces a value of 0.52700 for the second group 1408associated with males age 55-64, then this value is associated by theexample visitor assignor 1108 to fall within a visitor (v) value of 1.As a result, the first group 1404 is deemed to have two visitors, eachhaving an age somewhere between 6-11, and the second group 1408 isdeemed to have one visitor having an age somewhere between the ages of55-64. The example random number generator 1106 is again employed torandomly assign corresponding ages for each of the two visitors from thefirst group 1404 between the ages of 6-11, and to randomly assign an agefor the visitor from the second group 1408 between the ages of 55-64.While the aforementioned example was performed for a target demographicgroup of interest of females between the ages of 6-11 and males betweenthe ages of 55-64, the same process may be repeated for all demographicgroups of interest to possibly assign other visitors to a given tuningsegment.

The program 1500 of FIG. 15 begins at block 1502 where the example PMinterface 202 acquires and identifies data associated with visitors thathave selected visitor button(s) of panelist households within a regionof interest (e.g., a DMA). The example weighting engine 210 appliesweights to the collected visitor data in proportions that are based onan amount of time since the date of collection of the donor data (block1504). As described above, index value data points that are more recentin time generally reside closer to an index value of 1.00 (see FIG. 3).In other words, an accuracy of the viewing index is better when thecorresponding collected data is more recent.

When performing an analysis of a market of interest, the examplecategorizer 206 categorizes the acquired PM data based on one or morecategories of interest (block 1506). As described above, categories ofinterest may include, but are not limited to an age/gender combinationof interest, a particular household size of interest, a particular lifestage of interest, a particular viewed station/affiliate/genre ofinterest, a particular daypart of interest, a number of television setsof interest within the household (e.g., households with one televisionset, households with 2-3 television sets, households with three or moretelevision sets, etc.), and/or an education level of the head ofhousehold. While a relatively large number of MMPM households 106 willhave at least one of the aforementioned categories, a substantiallysmaller number of MMPM households 106 will represent all of the targetcombination of categories of interest to a market researching during amarket study.

If the example visitor imputation engine 112 determines that a thresholdnumber of households associated with a preferred and/or otherwisedesired set of characteristics is satisfied (e.g., a threshold of atleast 30 households) (block 1508), then the AVP value(s) are calculatedby the example AVP calculator 1102 in a manner consistent with FIG. 12(block 1510). On the other hand, in the event the example visitorimputation engine 112 determines that a threshold number of householdsis not satisfied (block 1508), then the AVP value(s) are calculated bythe example AVP calculator 1102 in a manner consistent with FIG. 13(block 1512). In particular, the example AVP calculator 1102 and/or theexample distribution engine 230 calculates an exposure proportion foreach category of interest, and calculates a tuning proportion for eachcategory of interest. The product of each calculated category-specificexposure proportion and total exposure minutes yields expected exposureminutes, and the product of each calculated category-specific tuningproportion and total tuning minutes yields expected tuning minutes. Theresulting expected exposure minutes and expected tuning minutes areapplied to example Equation 8 to generate corresponding AVP values.

The example distribution engine 1104 defines a distribution model toapply, such as the Poisson distribution (block 1514), and calculatesprobabilities for any number of visitors (v) of interest in a mannerconsistent with example Equation 10 (block 1516). For example, FIG. 14illustrates eleven (11) different number of visitor values 1402 fromzero (0) to ten (10). The example distribution engine 1104 alsocalculates cumulative probabilities so that selections from thedistribution can be selected from values bounded between zero (0) andone (1) (block 1518). The example distribution engine 1104 invokes therandom number generator 1106 to select a corresponding number ofvisitors (v) from the cumulative probabilities set for each demographicset of interest (block 1520). Once each demographic set of interest hasa determined number of visitors, bounded age values are randomlyselected for each visitor to be associated with tuning minutes (block1522).

Ambient Tuning

As described above, employing a MM without a PM to characterizehousehold media exposure behavior facilitates substantial cost savingswhen compared to employing PM devices, which may be physically connectedto media devices (e.g., televisions) and require professionalinstallation. For example, a MM may be mailed to a panelist, plugged into power and function without professional installation and/or withoutconnection to the panelist's electronics (e.g., media electronics suchas DVD players, set top boxes, televisions, etc.). Although using MMdevices without PMs result in substantial panelist household costsavings, some households have two or more media devices located in roomsin a relative proximity to where sound from a first media device reachesthe room in which the second media device is located, and vice versa. Insuch circumstances, a MM device from the first room may incorrectlycredit exposure minutes based on audio spillover associated with thesecond media device in the second room (and vice versa). When MM devicesincorrectly credit exposure minutes, one or more household tuningestimates and/or projections may be overreported/inflated. Examplemethods, apparatus, systems and/or articles of manufacture disclosedherein distinguish instances of ambient tuning (e.g., due to spillover)from instances of real tuning.

FIG. 16 is a schematic illustration of an example implementation of theexample ambient tuning engine 120 of FIG. 1. The example ambient tuningengine 120 of FIG. 1 is constructed in accordance with the teachings ofthis disclosure. In the illustrated example of FIG. 16, the ambienttuning engine 120 includes the PM interface 202 and the MM interface 204as disclosed above in connection with FIG. 2. Additionally, theillustrated example of FIG. 16 includes a simultaneous tuning monitor1602, a crediting manager 1604, a station comparator 1606, a tuning typeassignor 1608, a modeling engine 1614, a code stacking manager 1616, anautomatic gain control (AGC) monitor 1610 and a code presence manager1612.

In operation, the example PM interface 202 and the example MM interface204 collect household tuning data from MMPM households 106 and MMHhouseholds 108 within a region of interest 104 (e.g., panelisthouseholds within a direct marketing area (DMA)) that comprise anavailable data pool (e.g., LPM households, NPM households, etc.). Theexample ambient tuning engine 120 invokes the example simultaneoustuning monitor 1602 to identify whether instances of simultaneous tuningminutes from collected household data are either ambient or real. Asused herein, “simultaneous tuning” refers to instances where two or moremeters within a household are detecting the same media (e.g., the sametelevision station). To illustrate, assume a first MM proximate to afirst television in a first room detects station WAAA, and a second MMproximate to a second television in a second room also detects stationWAAA. One possibility that may be true is that both media devices (e.g.,televisions) are powered on and tuned to station WAAA. However, anotherpossibility is that the first television is on and tuned to station WAAAwhile the second television is tuned to another station while muted. Yetanother possibility is that the first television is on and tuned tostation WAAA while the second television is not powered on. In suchcircumstances, the second MM device may be detecting audio (e.g.,spillover) from the first television and, thus, improperly inflatingmedia exposure (e.g., consumption) metrics associated with the secondtelevision and/or household members.

In some examples, the crediting manager 1604 identifies quantities oftime (e.g., minutes) where the MM device credited a station, and theexample station comparator 1606 determines whether an AP device pairedwith the MM device is also crediting the same station. If so, then theexample tuning type assignor 1608 assigns the corresponding tuningminute as real. On the other hand, if the example crediting manager 1604identifies minutes where the MM devices credited a station (e.g.,embedded codes detected by the MM device, embedded codes passed-on bythe MM device and detected by the ambient tuning engine 120 duringpost-processing, signature post processing, etc.), and the examplestation comparator 1606 determines that the paired AP device is nottuned to the same station, then the example station comparator 1606determines whether a separate metering device within the household istuned to the same station, such as another AP and/or MM deviceassociated with a second television in a second room of the household.If so, then that household tuning minute is deemed and/or otherwiselabeled as ambient tuning/spillover, which should be ignored to preventimproper overrepresentation. On the other hand, in the event the examplestation comparator 1606 determines that no other metering device in thehousehold is also tuned to the same station, then the example tuningtype assignor 1608 assigns the minute as non-tuning. The examplesimultaneous tuning monitor 1602 continues to evaluate each receivedtuning minute within the pool of data collected from the examplepanelist households 104.

To develop a stochastic approach to determine the occurrence ofspillover in which derived model coefficients are derived for use in MMHhouseholds 108, the example ambient tuning engine 120 collectsadditional predictive variables indicative of the occurrence ornon-occurrence of spillover. The predictive variables are applied to amodel, such as a regression model, to generate coefficients/parametersthat facilitate calculation of a probability that spillover is occurringor not occurring within the MMH households 108. At least threepredictive variables indicative of the occurrence or non-occurrence ofspillover include automatic gain control (AGC) values, the presence ofembedded codes, such as final distributor audio codes (FDACs), and theduration of the collected segment.

Generally speaking, by comparing AGC values between two separate MMdevices within a household (e.g., calculating a differencetherebetween), an indication of spillover may be evaluated. A MM deviceplaced relatively close to a first television, for example, is morelikely to have a low AGC value because of a higher relative volume whencompared to an AGC value associated with sound from a televisionrelatively farther away. AGC values are typically established byacoustic gain circuits that apply greater gain (e.g., amplification)when attempting to discern and/or otherwise detect sound energy that hasa relatively low volume than when attempting to detect sound energy of ahigher volume. Volume may be lower, for example, due to a greaterdistance from a source of the originating sound energy. Additionally,quantities and/or densities of detected codes per unit of time areadditional example predictive variable(s) that may be applied to themodel to derive an indication of the likelihood of the occurrence ornon-occurrence of spillover. Without limitation, segment duration isanother predictive variable useful in the indication of spillover, asdescribed in further detail below.

The example AGC monitor 1610 of FIG. 16 assigns each collected minute toa corresponding AGC value. The example code presence manager 1612 ofFIG. 16 assigns each collected minute an indicator corresponding to thepresence or absence of an embedded code. In some examples, codedetection activit(ies) may occur during post processing of raw audioinformation collected by meter(s). In other examples, the codes aredetected in real time or near real time. The example ambient tuningengine 120 of FIG. 16 segregates instances of simultaneous tuningminutes based on whether embedded codes have been detected. For example,the modeling engine 1614 prepares a regression model with dependentvariables reflecting the previously determined real or ambient statusoccurrence(s). The example AGC monitor 1610 determines a minimum (e.g.,lowest) AGC for the household devices for a particular monitored timeperiod and/or collected set of audio data. For each device and minute,the example AGC monitor 1610 determines an AGC difference value withrespect to the minimum AGC value and each collected minute.

The example code presence manager 1612 of FIG. 16 identifies one ofthree possible scenarios for the type and presence of codes in collectedMM data for devices (e.g., TV sets, radio, etc.) within a household. Afirst possible scenario is that no codes are present in the collected MMdata for any of the devices of the household of interest. A secondpossible scenario is that the collected MM data has some codes for someof the devices within the household, but not all of the devices haveassociated codes detected in the collected minutes. A third possiblescenario is that the collected MM data for the household has codes inall of the data collected. In other words, each collected minute oftuning data has corresponding codes in all devices within thathousehold.

If none of the meters within the household have collected codes in thecollected minutes, then the example simultaneous tuning monitor 1602 ofFIG. 16 places a greater weight on a type of segment duration for thehousehold. For instance, if a television is tuned to station WAAA, thenthe MM device closest to that television will have a relatively longercollected segment duration than a MM device located further away fromthe television. The sound emanating from a television located furtheraway from that same MM device may fluctuate in intensity such that theMM device may not capture full segment durations. The examplesimultaneous tuning monitor 1602 of FIG. 16 identifies, for eachhousehold, a longest (e.g., maximum) segment duration and calculates aduration difference to be applied to the logistic regression fit of thecollected data in a manner consistent with example Equation (11).

$\begin{matrix}{{{Log}\left\lbrack \frac{{Probability}\mspace{14mu}\left( {{{Simultaneous}\mspace{14mu}{Minute}} = {Ambient}} \right)}{{Probability}\mspace{14mu}\left( {{{Simultaneous}\mspace{14mu}{Minute}} = {Real}} \right)} \right\rbrack} = {B_{0} + {B_{1}X_{1}} + \ldots + {B_{k}X_{k}}}} & {{Equation}\mspace{14mu}(11)}\end{matrix}$In the illustrated example of Equation (11), the model has the response(dependent) dependent variable as the ambient or real value to whicheach simultaneous tuning minute is assigned. Independent variables X₁, .. . , X_(k) may be coded and/or otherwise categorized with modelcoefficients B₁, . . . , B_(k). Categories may be represented by anyscale, such as AGC values ranging from zero to one hundred havingsub-groups therein.

If some of the meters within the household have collected codes in thecollected minutes (e.g., collected raw audio with codes embedded thereinand subsequently identified during audio data post processing), butothers do not, then the example modeling engine 1614 of FIG. 16 builds amodel in a manner consistent with example Equation (11) using dataassociated with the AGC difference values. If all of the meters withinthe household have collected codes in the collected minutes, then theexample code stacking manager 1616 determines a maximum unstacked countand a maximum stacked count for the household devices. As used herein, astacked code refers to an instance of code repair and/or imputation whenpart of a code is detected. In such cases where the entire code contentis not correctly collected by the MM device, a stacking procedurefills-in portions of the code that were not detected. Generallyspeaking, meter devices (e.g., MMs) that are relatively closer to themedia device (e.g., television) will have a better ability to collectunstacked codes that are not in need of repair or padding due to, forexample, a relatively closer proximity to the meter device(s). However,when the meter devices operate at a distance relatively farther awayfrom the monitored device, the ability for the meter devices toaccurately collect the entire code becomes more difficult and erroneous.The code stacking manager 1616 of the illustrated example determines adifference between meter devices within the household of the stacked andunstacked count values, which is applied to the model. Additionally, thesimultaneous tuning monitor 1602 of the illustrated example identifies amaximum average of seconds of collected code for all meter deviceswithin the household, and calculates a difference between thosehousehold devices. The difference of seconds of collected code, thestacked and unstacked code count difference values, and the AGCdifference values are applied to the example model of Equation (11) toderive the corresponding model coefficients (e.g., B1, . . . , Bk).

As described above, the example code presence manager 1612 of FIG. 16identifies one of the three possible scenarios for the type and presenceof codes in the household and, based on the detected scenario, applies adifferent combination of predictive variables (e.g., AGC values, segmentduration, count of stacked versus unstacked codes). Each of thesescenarios applies the corresponding predictive variables to the examplemodel of Equation (11), and the example modeling engine 1614 of FIG. 16calculates a probability of spillover in a manner consistent withexample Equation (12).

$\begin{matrix}{{{Probability}\mspace{14mu}\left( {{Minute} = {Ambient}} \right)} = \frac{1}{\left\lbrack {1 + {\mathbb{e}}^{- {({B_{0} + {B_{1}X_{1}} + \;\ldots\; + {B_{k}X_{k}}})}}} \right\rbrack}} & {{Equation}\mspace{14mu}(12)}\end{matrix}$Each simultaneous tuning minute may be identified as either ambienttuning or real tuning based on the resulting probability value and athreshold established by, for example, a market researcher. For example,if the probability value is greater than or equal to 0.50, then theminute may be designated as ambient tuning. On the other hand, theminute may be designated as real tuning for probability values less than0.50.

The program 1700 of FIG. 17 begins at block 1702 where the example PMinterface 202 and the example MM interface 204 of the illustratedexample collect tuning data from MMPM households 106 and MMH households108 within panelist households 104. The example simultaneous tuningmonitor 1602 of FIG. 16 identifies whether simultaneous tuning minuteswithin such households are either ambient or real (block 1704), asdescribed in further detail below in connection with FIG. 18.

FIG. 18 includes additional detail from the illustrated example of FIG.17. When identifying whether simultaneous tuning minutes are ambient orreal, the example crediting manager 1604 of FIG. 16 identifies minuteswhere a MM device (e.g., a MM device in the MMPM household 106) withinthe household of interest credited a station (block 1802). The stationcomparator 1606 of FIG. 16 determines whether an AP device in thehousehold of interest is also crediting the same station as the MMdevice at the same time (block 1804). In some examples, the creditingmanager 1604 compares a timestamp associated with minutes collected fromthe MM device with a timestamp associated with minutes collected fromthe PM device of the same household. If the timestamps match and thedetected stations are the same, then the example tuning type assignor1608 of FIG. 16 assigns that corresponding minute as real tuning (block1806). The example simultaneous tuning monitor 1602 determines if thereare additional minutes from the household of interest to analyze (block1808). If so, then the example simultaneous tuning monitor 1602 selectsthe next minute for analysis (block 1810) and control returns to block1804.

If the example station comparator 1606 of FIG. 16 determines that the APdevice is not crediting the same station as the MM device within thehousehold (block 1804), which could be due to multiple mediapresentation devices (e.g., TV sets) within the household being tuned todifferent stations or turned off, then the example station comparator1606 of the illustrated example determines whether the other device istuned to the same station (block 1812). As described above, examplemethods, apparatus, systems and/or articles of manufacture disclosedherein employ MMPM households 106 that have both MM devices and PMdevices so that ambiguity of actual device behavior is eliminated. Oncemodel coefficients are generated based on such observed behaviors in theMMPM households 106, the data collected from the MMH households 108 maybe imputed with the coefficients to allow an indication of spillover tobe calculated. As such, panelist households without PMs can beeffectively utilized. As a result, a greater number of panelisthouseholds may be implemented in the example region of interest 104without the added capital expense of PM devices that requireprofessional installation, relatively greater training, and/or moreroutine maintenance than MM devices.

If the example station comparator 1606 determines that the other devicein the household is tuned to the same station (block 1812) (e.g., basedon the detection of the same codes), then the example tuning typeassignor 1608 assigns the corresponding minute as ambient tuning (alsoreferred to herein as spillover) (block 1814). On the other hand, if theexample station comparator 1606 determines that the other device in thehousehold is not tuned to the same station (block 1812), then theexample tuning type assignor 1608 of the illustrated example assigns thecorresponding minute as a non-tuning minute (block 1816).

Returning to FIG. 17, the example AGC monitor 1610 of the illustratedexample assigns each minute to a corresponding AGC value (block 1706).As described above, the AGC value associated with a collected minute insome example predictive variables assist in calculating a probability ofambient tuning occurrences. Additionally, another example predictivevariable discussed above includes the presence or absence of embeddedcodes within the collected minute of media. The example code presencemanager 1612 of the illustrated example assigns each minute an indicatorregarding the presence or absence of embedded codes (block 1708). Theexample ambient tuning engine 120 segregates instances of simultaneoustuning minutes based on whether such embedded codes have been detected(block 1710), as described further below in connection with FIG. 19.

In the illustrated example of FIG. 19 (block 1710), the modeling engine1614 of the illustrated example prepares a regression model withdependent variables reflecting corresponding real or ambient statusindicators (block 1902). For each household device of interest, theexample AGC monitor 1610 determines a minimum AGC value across two ormore MM devices (block 1904) and determines a difference valuetherebetween (block 1906). In view of the possibility that the MMdevices within the household of interest may either collect no codes,collect some codes for some of the minutes and not others, or collectcodes for all of the minutes, the example code presence manager 1612 ofthe illustrated example identifies which circumstance applies (block1908).

If the example code presence manager 1612 of the illustrated exampleidentifies a first category in which no codes are detected, the examplesimultaneous tuning monitor 1602 determines a maximum segment durationassociated with the MM devices (block 1910), and calculates a differencetherebetween (block 1912). The example modeling engine 1614 of FIG. 16applies a logistic regression fit to the collected data in a mannerconsistent with example Equation (11) (block 1914), as described above.In particular, when the household does not detect any codes in thecollected minutes, the example model of Equation (11) is tailored toconsider (1) the AGC values and (2) differences in collected segmentdurations (block 1914).

If the example code presence manager 1612 of the illustrated exampleidentifies a second category in which some codes are detected in someminutes, while no codes are detected in other minutes (block 1908), thenthe example modeling engine 1614 of FIG. 16 applies a logisticregression fit to the collected data in a manner consistent with exampleEquation (11) (block 1916). However, in this application of exampleEquation (11), the model employs (1) the AGC values and (2) the presenceor absence of codes in corresponding collected minutes (block 1916).

If the example code presence manager 1612 identifies a third category inwhich all codes are detected in all collected minutes (block 1908), thenthe example code stacking manager 1616 of this example determineswhether the detected codes are, themselves, complete (block 1918). Asdescribed above, while the example MM devices may detect and/orotherwise capture codes that may have been embedded in media from amedia device (e.g., a television), the quality of the detected codes maydiffer. Such differences may be due to, for example, the MM devicecollecting audio from a television that is relatively far away fromwhere the MM device is located. In such situations, one or more stackingoperations may supplement missing portions of the detected code withaccurate code data. The example code stacking manager 1616 of thisexample identifies a difference between MM devices in the householdregarding the number of stacked codes versus unstacked codes detected(block 1920). Additionally, the example simultaneous tuning monitor 1602calculates an average (e.g., a maximum average) seconds of code permetering device in the household, and a corresponding difference valuebetween each metering device (block 1922). The example modeling engine1614 of the illustrated example applies the logistic regression fit tothe collected data in a manner consistent with example Equation (11)(block 1924). However, in this application of example Equation (11), themodel employs (1) the AGC values, (2) the differences betweenstacked/unstacked embedded codes and (3) the differences between theaverage number of seconds of code between metering devices (block 1924).

Returning to FIG. 17, the example modeling engine 1614 of this exampleapplies calculated coefficients from the model (e.g., Equation (11)) toa probability calculation in a manner consistent with example Equation(12) to determine a probability that tuning for a given minute should becategorized as spillover (ambient tuning) (block 1712).

On/Off Detection

As described above, employing a MM to characterize household mediaviewing behavior may be performed in a stochastic manner rather than byemploying a PM to save money that would otherwise be spent on therelatively expensive PM devices. When a MM device is employed to collectaudio signal (tuning) data from a household, some of the collectedminutes may include codes (e.g., embedded codes collected in the rawaudio and passed on to the on/off detection engine 130 for postprocessing), some of the collected minutes may be analyzed via signatureanalysis (e.g., analysis of the raw audio collected by the MM device andpassed on to the on/off detection engine 130 for audio signaturecomparison against one or more signature database(s)), and some of thecollected minutes may have neither codes nor have correspondingsignature matches for media identification.

FIG. 20 illustrates an example crediting chart 2000 having a block oftwenty-four (24) hours of tuning data collected from an example MMdevice in an example household. In the illustrated example of FIG. 20,some portions of collected minutes from the household are associatedwith codes 2002, which also indicates that a media device (e.g., atelevision) within the household is turned on. Additionally, someportions of collected minutes from the household are associated withsignatures of the detected media 2004 that, when compared to a referencedatabase, allow identification of media. However, still other portionsof collected minutes from the household have neither codes norsignatures that match known media in a reference database 2006.

Minutes that have neither codes nor corresponding signatures that may beused with a reference database are referred to herein as all othertuning (AOT) minutes. In such circumstances with a PM device, the mediadevice (e.g., television) will be detected in an on state (e.g., powerstatus ON based on a power status detector of the PM device), but nostation and/or media can be credited with tuning. In othercircumstances, a media device may be in a muted state or an off state(e.g., power status OFF), thus no audio is emitted that can be used forcrediting. Example methods, systems, apparatus and/or articles ofmanufacture disclosed herein apply a stochastic manner of determiningwhether AOT minutes are associated with an off state or an on state,which may be associated with other media device usage separate frommedia programming (e.g., video game usage, video conferencing, etc.).

FIG. 21 is a schematic illustration of the example on/off detectionengine 130 of FIG. 1 and constructed in accordance with the teachings ofthis disclosure. In the illustrated example of FIG. 21, the on/offdetection engine 130 includes the PM interface 202, the MM interface204, the AGC monitor 1610 and the modeling engine 1614 as disclosedabove in connection with FIGS. 2 and 16.

In operation, the example PM interface 202 collects minutes from a PMdevice (e.g., an active/passive people-meter) within the householdrelated to three categories of media device usage. A first category ofmedia device usage associated with some collected minutes is related toa particular station or media, such as media identified by way of codesor signature matching. A second category of media device usageassociated with other collected minutes is related to instances ofnon-programming related usage, such as video game play, videoconferencing activity, home picture viewing, etc. A third category ofmedia device usage associated with still other collected minutes isrelated to instances where the media device is powered off.

The example MM interface 204 also collects minutes from a MM devicewithin the household. As described above, because the MM interface 204is not physically connected to the media device, it cannot directlyverify whether the media device is powered on and, instead, collectsonly audio-based information via one or more built-in microphones. Theexample MM interface 204 may collect minutes data that either credits astation or media, or designates the collected minutes as AOU. Theexample AGC monitor 1610 collects AGC values from each of the example MMinterface 204 and the example PM interface 202 for each correspondingminute, and the example modeling engine 1614 prepares a regression modelto fit the collected data in a manner consistent with example Equation(13).

$\begin{matrix}{{{Log}\left\lbrack \frac{{Probability}\mspace{14mu}\left( {{{Given}\mspace{14mu}{Minute}} = {H\; U\; T}} \right)}{{Probability}\mspace{14mu}\left( {{{Given}\mspace{14mu}{Minute}} = {OFF}} \right)} \right\rbrack} = {B_{0} + {B_{1}X_{1}} + \ldots + {B_{k}X_{k}}}} & {{Equation}\mspace{14mu}(13)}\end{matrix}$In the illustrated example of Equation (13), HUT is indicative of a“household using television” on (e.g., an ON power status), off isindicative of an OFF power status, and the independent variables (X)include AGC values, daypart information and/or a number of minutes sincea code reader credit occurred.

The example modeling engine 1614 uses derived coefficients (B) tocalculate a probability for each minute as either on (HUT) or off in amanner consistent with example Equation (14).

$\begin{matrix}{{{Probability}\mspace{14mu}\left( {{{AOU}\mspace{14mu}{Minute}} = {H\; U\; T}} \right)} = \frac{1}{\left\lbrack {1 + {\mathbb{e}}^{- {({B_{0} + {B_{1}X_{1}} + \;\ldots\; + {B_{k}X_{k}}})}}} \right\rbrack}} & {{Equation}\mspace{14mu}(14)}\end{matrix}$

The program 2200 of FIG. 22 begins at block 2202 where the example PMinterface 202 collects minutes from the PM device related to minuteswhere a station was credited, minutes where the television was in use,but had no crediting, and where the television was powered off. Theexample MM interface 204 collects minutes from the MM device in the dualpanel household related to minutes where a station was credited, andminutes of AOU (block 2204). The example AGC monitor 1610 collects AGCvalues associated with each minute collected by the example PM interface202 and MM interface 204 (block 2206).

The example modeling engine 1614 prepares a model based on AGC values,day parts and a number of minutes since a last MM device credit in amanner consistent with example Equation (13) (block 2208). The model mayinclude, but is not limited to, a regression model, in whichcoefficients may be derived after fitting the collected data. Thederived model coefficients are used by the example modeling engine 1614to calculate a probability that any particular minute of interest wasassociated with either an on state or an off state of the householdmedia device. These derived coefficients may be associated with panelisthouseholds within the region of interest 104 having only MM devices 108(block 2210).

FIG. 23 is a block diagram of an example processor platform 2300 capableof executing the instructions of FIGS. 6-9, 15, 17-19 and 22 toimplement the ambient tuning engine 120, the imputation engine 110, thevisitor imputation engine 112, and the on/off detection engine 130 ofFIGS. 1, 2, 11, 16 and 21. The processor platform 2300 can be, forexample, a server, a personal computer, an Internet appliance, a digitalvideo recorder, a personal video recorder, a set top box, or any othertype of computing device.

The processor platform 2300 of the illustrated example includes aprocessor 2312. The processor 2312 of the illustrated example ishardware. For example, the processor 2312 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 2312 of the illustrated example includes a local memory2313 (e.g., a cache). The processor 2312 of the illustrated example isin communication with a main memory including a volatile memory 2314 anda non-volatile memory 2316 via a bus 2318. The volatile memory 2314 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 2316 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 2314,2316 is controlled by a memory controller.

The processor platform 2300 of the illustrated example also includes aninterface circuit 2320. The interface circuit 2320 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 2322 are connectedto the interface circuit 2320. The input device(s) 2322 permit(s) a userto enter data and commands into the processor 2312. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 2324 are also connected to the interfacecircuit 2320 of the illustrated example. The output devices 2324 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a lightemitting diode (LED), a printer and/or speakers). The interface circuit2320 of the illustrated example, thus, typically includes a graphicsdriver card, a graphics driver chip or a graphics driver processor.

The interface circuit 2320 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network2326 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 2300 of the illustrated example also includes oneor more mass storage devices 2328 for storing software and/or data.Examples of such mass storage devices 2328 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 2332 of FIGS. 6-9, 15, 17-19 and 22 may be storedin the mass storage device 2328, in the volatile memory 2314, in thenon-volatile memory 2316, and/or on a removable tangible computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that the above disclosedmethods, apparatus and articles of manufacture allow audiencemeasurement techniques to occur with a substantially larger quantity ofhouseholds, in which each household has a substantially lower meteringequipment cost by employing audio-based code reader devices instead ofrelatively more expensive people meter devices. Examples disclosedherein permit a determination of behavior probability that can beapplied to households that do not have a People Meter device and,instead, employ a media meter that captures audio without a physicalconnection to a media device (e.g., a television). Such examples allowbehavior probability calculations based on utilization of otherhouseholds that include the People Meter device, in which thecalculations reveal behavior probabilities in a stochastic manner thatadheres to expectations of statistical significance.

Example methods, systems, apparatus and/or articles of manufacturedisclosed herein also facilitate a stochastic manner of determining aprobability of ambient tuning within households that do not employ aPeople Meter device. In some examples disclosed herein, both a panelistaudience meter (e.g., a People Meter) and a media meter (e.g., capturesaudio without a physical connection to a media device) are employed toobtain data associated with media code status and one or more automaticgain control (AGC) values. Based on the obtained code status and AGCvalues, examples disclosed herein create model coefficients that may beapplied to households with only media meters in a manner that determinesa probability of ambient tuning that upholds expectations of statisticalsignificance. Additionally, data obtained related to AGC values aredisclosed herein to be used with daypart information to calculate modelcoefficients indicative of whether a media device (e.g., a television)is powered on or powered off.

Additional example methods, systems, apparatus and/or articles ofmanufacture disclosed herein identify probabilities of a number ofvisitors in a household and their corresponding ages. In particular,examples disclosed herein calculate an average visitor parameter (AVP)based on exposure minutes and tuning minutes, which are further appliedto a Poisson distribution to determine a probability of having a certainnumber of visitors in a household. Such probabilities are in view of atarget demographic of interest having a particular age range, which maybe selected based on inputs from a random number generator.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method to calculate a probability of a firstmedia device having a first power status, comprising: identifying, witha processor, a power status and a first automatic gain control (AGC)value for an exposure minute from a panelist audience meter in a firsthousehold, the panelist audience meter including a power sensor to sensea power status of a second media device in the first household;identifying, with the processor a second AGC value of a first mediameter (MM) during a household tuning minute in the first household, thefirst MM including a microphone to collect audio data; and preventingerroneous crediting by: calculating, with the processor, modelcoefficients based on the exposure minute and the household tuningminute; calculating, with the processor, a power status probability forthe first media device in a second household based on the modelcoefficients and data from a second MM in the second household, thesecond household not including a panelist audience meter having a powersensor; and determining whether to credit a media exposure identified bythe second MM in the second household based on the power statusprobability.
 2. A method as defined in claim 1, wherein the data fromthe second MM includes daypart information and AGC values from thesecond MM in the second household.
 3. A method as defined in claim 1,wherein the calculating of the model coefficients further includescalculating the model coefficients with an independent variable based ona number of minutes since the first MM in the first household credited astation.
 4. A method as defined in claim 1, further includingidentifying a number of minutes associated with the first media devicein the second household and containing neither codes nor signatureshaving a match with a reference database.
 5. A method as defined inclaim 4, wherein the calculating of the power status probabilitydetermines whether the number of minutes associated with the first mediadevice in the second household is associated with an OFF power state oran all other tuning state.
 6. A method as defined in claim 4, whereinthe calculating of the power status probability determines whether thenumber of minutes associated with the first media device in the secondhousehold is associated with a muted state.
 7. A method as defined inclaim 4, further including attributing the number of minutes containingneither codes nor signatures to video game usage.
 8. A method as definedin claim 1, further including determining daypart information associatedwith the household tuning minute.
 9. An apparatus to calculate aprobability of a first media device being in a first power state,comprising: an automatic gain control (AGC) monitor to identify a firstAGC value of a panelist audience meter during an exposure minute in afirst household, the panelist audience meter including a power sensor tosense a power status of a second media device in the first household,the AGC monitor to identify a second AGC value of a first media meter(MM) during a household tuning minute in the first household, the firstMM including a microphone to collect audio data; and a modeling engineto prevent erroneous crediting by: calculating model coefficients basedon the exposure minute and the household tuning minute; calculating apower status probability for the first media device in a secondhousehold based on the model coefficients and data from a second MM inthe second household, the second household not including a the panelistaudience meter having a power sensor; and determining whether to credita media exposure identified by the second MM in the second householdbased on the power status probability.
 10. An apparatus as defined inclaim 9, wherein the data from the second MM includes daypartinformation and AGC values.
 11. An apparatus as defined in claim 9,wherein the modeling engine is to calculate the model coefficients withan independent variable based on a number of minutes since the first MMin the first household credited a station.
 12. An apparatus as definedin claim 9, further including a detection engine to identify a number ofminutes associated with the first media device in the second householdand containing neither codes nor signatures having a match with areference database.
 13. An apparatus as defined in claim 12, wherein themodeling engine is to determine whether the number of minutes associatedwith the first media device in the second household is associated withan OFF power state or an all other tuning (AOT) state based on the powerstatus probability calculation.
 14. An apparatus as defined in claim 12,wherein the modeling engine is to determine whether the number ofminutes associated with the first media device in the second householdcontaining neither codes nor signatures is associated with a mutedstate.
 15. An apparatus as defined in claim 12, wherein the modelingengine is to attribute the number of minutes containing neither codesnor signatures to video game usage.
 16. An apparatus as defined in claim9, wherein the AGC monitor is to identify daypart information associatedwith the household tuning minute.
 17. A tangible machine readablestorage medium comprising instructions that, when executed, cause amachine to at least: identify a power status and a first automatic gaincontrol (AGC) value for an exposure minute from a panelist audiencemeter in a first household, the panelist audience meter including apower sensor to sense a power status of a first media device in thefirst household; identify a second AGC value of a first media meter (MM)during a household tuning minute in the first household, the first MMincluding a microphone to collect audio data; and prevent erroneouscrediting by: calculating model coefficients based on the exposureminute and the household tuning minute; calculating a power statusprobability for a second media device in a second household based on themodel coefficients and data from a second MM in the second household,the second household not including a panelist audience meter having apower sensor; and determining whether to credit a media exposureidentified by the second MM in the second household based on the powerstatus probability.
 18. A storage medium as defined in claim 17, whereinthe instructions, when executed, further cause the data from the secondMM to include daypart information and AGC values.
 19. A storage mediumas defined in claim 17, wherein the instructions, when executed, causethe machine to calculate the model coefficients with an independentvariable based on a number of minutes since the first MM in the firsthousehold credited a station.
 20. A storage medium as defined in claim17, wherein the instructions, when executed, further cause the machineto identify a number of minutes associated with the second media devicein the second household and containing neither codes nor signatureshaving a match with a reference database.
 21. A storage medium asdefined in claim 20, wherein the instructions, when executed, furthercause the machine to determine whether the number of minutes associatedwith the second media device in the second household is associated withan OFF power state or an all other tuning state based on the powerstatus probability calculation.
 22. A storage medium as defined in claim20, wherein the instructions, when executed, further cause the machineto determine whether the number of minutes associated with the secondmedia device in the second household containing neither codes norsignatures is associated with a muted state.
 23. A storage medium asdefined in claim 17, wherein the instructions, when executed, furthercause the machine to determine daypart information associated with thehousehold tuning minute.